Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" of they form.
+
Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" they form.
Often, a cluster of terms characterizes a topic.
@@ -617,7 +624,7 @@
Why semantic networks?
nodes are words ("USA") or groups of words ("United States of America")
-
relations are, usually, signifying a co-occurrences: two words are connected if they co-occur.
+
relations are, usually, signifying co-occurrences: two words are connected if they appear in the same document, or in the same paragraph, or same sentence… you decide.
@@ -699,7 +706,7 @@
2 bis. Considering "noun phrases"
-
"delete all in the text except for all groups of words made of nouns and adjectives, ending by a noun"
+
"delete all in the text except for groups of words made of nouns and adjectives, ending by a noun"
@@ -845,6 +852,176 @@
2. Representing only the m
tf-idf can be left for specialists of the textual data under consideration, after they have been presented with the simple frequency count version.
+
+
+
+
Computing connections (edges) in the network
+
+
+
We now have extracted the most interesting / meaningful terms from the text.
+How to decide which connections make sense between them?
+
+
+
1. Co-occurrences
+
+
Connections between terms are usually drawn from co-occurrences: two terms will be connected if they appear next to each other in some pre-defined unit of text:
+
+
+
+
+
in the same sentence
+
+
+
in the same paragraph
+
+
+
in the same document (if the corpus is made of several documents)
+
+
+
+
+
(note on vocabulary: in the following, we will call this a "unit of text").
+
+
+
For example, in bibliometrics (the study of the publications produced by scientists), this could give:
+
+
+
+
+
collect abstracts (short summaries) of all scientific articles discussing "nano-technologies".
+
+
+
so, abstracts are our units of text here.
+
+
+
two terms will be connected if they frequently appear in the same abstracts.
+
+
+
+
+
+
2. What "weight" for the edges?
+
+
An edge between two terms will have:
+
+
+
+
+
weight of "1" if these two terms co-occur in just one unit of text.
+
+
+
weight of "2" if they co-occur in two units of text.
+
+
+
etc…
+
+
+
+
+
The logic is simple, and yet there are some refinements to discuss. It will be up to you to decide what’s preferable:
+
+
+
If 2 terms appear several times in a given unit of text, should their co-occurences be counted several times?
+
+
An example to clarify. Let’s imagine that we are interested in webpages discussing nanotechnology.
+We want to draw the semantic network of the vocabulary used in these web pages.
+
+
+
A co-occurrence is: when 2 terms are used on the same web page.
+
+
+
Among the pages we collected, there is the Wikipedia page discussing nanotechnology:
+
+
+
+
+
Nanotechnology ("nanotech") is manipulation of matter on an atomic, molecular, and supramolecular scale.
+The earliest, widespread description of nanotechnology referred to the particular technological goal of precisely manipulating atoms and molecules for fabrication of macroscale products, also now referred to as molecularnanotechnology
should I count only one co-occurrence between molecular and nanotechnology, because it happened on this one web page?
+
+
+
or should I consider that molecular appears twice on this page, and nanotechnology three times, so multiple co-occurrences between these 2 terms should be counted, just on this page already?
+
+
+
+
+
There is no exact response, and you can experiment with both possibilities.
+
+
+
+
If two terms are very frequent, is their co-occurrence really of interest?
So, in their study the unit of text was the "recipe", and the terms in the semantic network where ingredients in all these recipes.
+
+
+
Just because they are so common, some ingredients (like flour, sugar, salt) are bound to appear more frequently in the same recipes (to co-occur), than infrequent ingredients.
+
+
+
The authors of this study chose to highlight complementary ingredients: some ingredients appear often used together in the same recipes, even if they are ingredients which are quite rarely used.
+
+
+
"Complementary" here means that these ingredients have some interesting relationship: when one is used, the other "must" be used as well.
+
+
+
If we just count co-occurrences, this special relationship between infrequent complementary ingredients will be lost: by definition, 2 infrequent ingredients can’t co-occurr often.
+
+
+
To fix this, a solution consists in comparing how many times the 2 ingredients co-occur, with how frequent they are in all recipes:
+
+
+
→ ingredients co-occurring each and every time they are used will have a large edge weight,
+
+
+
→ ingredients co-occuring many times, but also appearing many times in different recipes, will get a low edge weight.
+
+
+
A simple formula does this operation. For ingredients A and B:
+
+
+
+
weight of edge between A and B =
+nb of recipes where A & B co-occur
+divided by
+(total nb of recipes where A appear x total nb of recipes where B appear)
+
+
+
+
Logs are often added to this formula, which is called "Pointwise mutual information":
+
+
+
+\$PMI = log((p(A, B)) /(p(A) p(B)))\$
+
+
+
+
We now have nodes and their relations: a semantic network. Let’s see now how to visualize it in Gephi.
+
+
+
+
+
+
+
Visualizing semantic networks with Gephi
+
+
@@ -869,9 +1046,9 @@
the end
or visit the website for more tutorials
+
+ ]
diff --git a/docs/generated-pdf/subdir/converting-a-network-with-dates-into-dynamic_temp_pdf.md b/docs/generated-pdf/subdir/converting-a-network-with-dates-into-dynamic_temp_pdf.md
new file mode 100644
index 0000000..81e25de
--- /dev/null
+++ b/docs/generated-pdf/subdir/converting-a-network-with-dates-into-dynamic_temp_pdf.md
@@ -0,0 +1,173 @@
+= Converting a network with dates into a dynamic network
+Clément Levallois
+2017-02-17
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:experimental:
+:sourcedir: ../../../../main/java
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Goals of this tutorial
+//ST: Goals of this tutorial
+
+//ST: !
+
+- We take a normal network, where nodes have attributes which can serve as time indication (a date, a number...)
+- We convert this network into a dynamic network: nodes will appear and disappear according to their attributes.
+
+== download a network file for practice
+//ST: download a network file for practice
+
+//ST: !
+
+link:../../resources/miserables-with-dates.zip[download this zip file] and unzip it on your computer.
+
+or use this direct link: https://tinyurl.com/gephi-tuto-4[https://tinyurl.com/gephi-tuto-4]
+
+//ST: !
+
+You should find the file `miserables-with-dates.gexf` in the zip file. Save it in a folder you will remember (or create a folder specially for this small project).
+
+//ST: description of the file / the network
+//ST: !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+//ST: !
+This file has been modified to add some dates to each character in the novel:
+
+- a "start date", which is a day (example: 22/09/1835). This is the date when the character *enters* the action in the novel
+- an "end date", also a day (example: 22/09/1840). This is the date when the character *leaves* the action in the novel
+- a "peak moment". This is a number (example: 14263). This is an instant when the character is at the center of the plot. This number has no historical meaning, this is just a chronological moment in time.
+
+//ST: !
+
+NOTE:: Values for start date, end date and peak moment have no real significance in the novel. They are made up for this exercise.
+
+== open the network in Gephi
+//ST: open the network in Gephi
+
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables-with-dates.gexf` on your computer and open it
+
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+{nbsp} +
+
+//ST: !
+A report window will open, giving you basic info on the network you opened:
+
+image::en/opening-file-report-en.png[align="center", title="report window"]
+{nbsp} +
+
+//ST: !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+//ST: !
+image::result_miserables.png[align="center",title="The network we will use"]
+{nbsp} +
+
+== getting a sense of the attributes in the data laboratory
+//ST: getting a sense of the attributes in the data laboratory
+
+//ST: !
+We can switch to the data laboratory to see the underlying data:
+
+image::Switching-to-the-data-laboratory.png[align="center",title="Switching to the data laboratory"]
+{nbsp} +
+
+
+//ST: !
+
+image::Zoom-on-three-attributes-representing-time.png[align="center",title="Zoom on three attributes representing time"]
+{nbsp} +
+
+
+//ST: !
+The nodes (characters) of the network have attributes (start date, end date, peak moment) which can make this graph dynamic - but it is not yet.
+
+A couple of steps are needed to enable the dynamic features, and here a choice must be made:
+
+//ST: !
+
+Do we prefer to have...
+
+1. ... nodes appearing on screen at their start date, and staying on screen for ever after?
+2. ... nodes appearing on screen at their start date, and leaving the screen at their end date?
+3. ... nodes being representedsimply by their "peak moment" (a number), without reference to chronological dates?
+
+We will present these 3 possibilities.
+
+//ST: !
+==== 1. dynamic nodes with a start date
+
+//ST: !
+
+image::Merge-columns.png[align="center",title="Merge columns"]
+{nbsp} +
+
+//ST: !
+
+image::Set-up-the-parameters---1.png[align="center",title="Set up the parameters - 1"]
+{nbsp} +
+
+//ST: !
+
+image::Result.png[align="center",title="Result"]
+{nbsp} +
+
+
+//ST: !
+Let's switch back to the Overview to see the graph and how it evolves in time.
+
+IMPORTANT:: We are going to use the timeline to play the animation. The timeline has many features which are explained in a specific tutorial.
+
+//ST: !
+
+
+{nbsp} +
+
+image::moving-timeline-miserables-2-en.png[align="center",title="Animating the dynamic network"]
+
+{github-root}images/moving-timeline-miserables-1-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-5
+
+
+== to be continued
+//ST: to be continued
+
+
+== more tutorials on dynamic networks with Gephi
+//ST: more tutorials on dynamic networks with Gephi
+
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-Dynamic-Data[The wiki on gephi.org]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/converting-a-network-with-dates-into-dynamic_temp_slides.md b/docs/generated-pdf/subdir/converting-a-network-with-dates-into-dynamic_temp_slides.md
new file mode 100644
index 0000000..0f1eae8
--- /dev/null
+++ b/docs/generated-pdf/subdir/converting-a-network-with-dates-into-dynamic_temp_slides.md
@@ -0,0 +1,192 @@
+= Converting a network with dates into a dynamic network
+Clément Levallois
+2017-02-17
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:experimental:
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Goals of this tutorial
+
+== !
+
+- We take a normal network, where nodes have attributes which can serve as time indication (a date, a number...)
+- We convert this network into a dynamic network: nodes will appear and disappear according to their attributes.
+
+== download a network file for practice
+
+== !
+
+link:../../resources/miserables-with-dates.zip[download this zip file] and unzip it on your computer.
+
+or use this direct link: https://tinyurl.com/gephi-tuto-4[https://tinyurl.com/gephi-tuto-4]
+
+== !
+
+You should find the file `miserables-with-dates.gexf` in the zip file. Save it in a folder you will remember (or create a folder specially for this small project).
+
+== description of the file / the network
+== !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+== !
+This file has been modified to add some dates to each character in the novel:
+
+- a "start date", which is a day (example: 22/09/1835). This is the date when the character *enters* the action in the novel
+- an "end date", also a day (example: 22/09/1840). This is the date when the character *leaves* the action in the novel
+- a "peak moment". This is a number (example: 14263). This is an instant when the character is at the center of the plot. This number has no historical meaning, this is just a chronological moment in time.
+
+== !
+
+NOTE:: Values for start date, end date and peak moment have no real significance in the novel. They are made up for this exercise.
+
+== open the network in Gephi
+
+== !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables-with-dates.gexf` on your computer and open it
+
+[.stretch]
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+
+
+== !
+A report window will open, giving you basic info on the network you opened:
+
+[.stretch]
+image::en/opening-file-report-en.png[align="center", title="report window"]
+
+
+== !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+== !
+[.stretch]
+image::result_miserables.png[align="center",title="The network we will use"]
+
+
+== getting a sense of the attributes in the data laboratory
+
+== !
+We can switch to the data laboratory to see the underlying data:
+
+[.stretch]
+image::Switching-to-the-data-laboratory.png[align="center",title="Switching to the data laboratory"]
+
+
+
+== !
+
+[.stretch]
+image::Zoom-on-three-attributes-representing-time.png[align="center",title="Zoom on three attributes representing time"]
+
+
+
+== !
+The nodes (characters) of the network have attributes (start date, end date, peak moment) which can make this graph dynamic - but it is not yet.
+
+A couple of steps are needed to enable the dynamic features, and here a choice must be made:
+
+== !
+
+Do we prefer to have...
+
+1. ... nodes appearing on screen at their start date, and staying on screen for ever after?
+2. ... nodes appearing on screen at their start date, and leaving the screen at their end date?
+3. ... nodes being representedsimply by their "peak moment" (a number), without reference to chronological dates?
+
+We will present these 3 possibilities.
+
+== !
+==== 1. dynamic nodes with a start date
+
+== !
+
+[.stretch]
+image::Merge-columns.png[align="center",title="Merge columns"]
+
+
+== !
+
+[.stretch]
+image::Set-up-the-parameters---1.png[align="center",title="Set up the parameters - 1"]
+
+
+== !
+
+[.stretch]
+image::Result.png[align="center",title="Result"]
+
+
+
+== !
+Let's switch back to the Overview to see the graph and how it evolves in time.
+
+IMPORTANT:: We are going to use the timeline to play the animation. The timeline has many features which are explained in a specific tutorial.
+
+== !
+
+[.stretch]
+image::moving-timeline-miserables-1-en.gif[align="center",title="Animating the dynamic network"]
+
+
+//PDF: image::moving-timeline-miserables-2-en.png[align="center",title="Animating the dynamic network"]
+
+//PDF: {github-root}images/moving-timeline-miserables-1-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-5
+
+
+== to be continued
+
+
+== more tutorials on dynamic networks with Gephi
+
+== !
+
+- https://github.com/gephi/gephi/wiki/Import-Dynamic-Data[The wiki on gephi.org]
+
+
+== The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_common.md b/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_common.md
new file mode 100644
index 0000000..fc76b9e
--- /dev/null
+++ b/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_common.md
@@ -0,0 +1,258 @@
+= Creating a network from a table of cooccurring items
+Clément Levallois
+2017-02-27
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+//ST: Presentation of the plugin
+
+//ST: !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+//ST: !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, each row describes an "occurrence" (of an event, a purchase, a relation, etc.)
+* In columns A, B, C, D, we have the entities involved: column A for persons, column B for what they bought, etc.
+* Connections will be created between entities, when they appear in the same occurrence (so, when they are on thee same row)
+* Occurrences can have dates, multiple instances of an entity can be listed in a given column.
+
+//ST: !
+==== 1. The input
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="An Excel file"]
+{nbsp} +
+
+
+//ST: !
+==== 2. The output
+//ST: !
+
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="Resulting network"]
+{nbsp} +
+
+== Installing the plugin
+//ST: Installing the plugin
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Opening the plugin
+//ST: Opening the plugin
+//ST: !
+
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+{nbsp} +
+
+== Using the plugin
+//ST: Using the plugin
+
+//ST: !
+==== 2nd panel
+//ST: !
+
+image::Select-a-file.png[align="center", title="Select a file"]
+{nbsp} +
+
+//ST: Is your file with a header?
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-2-en.png[align="center", title="A file without headers"]
+{nbsp} +
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="A file with headers"]
+{nbsp} +
+
+//ST: !
+To describe the next screens of the plugin, we will take the example of *the Excel file just shown*, with headers.
+
+//ST: !
+==== 3rd panel
+//ST: !
+
+image::en/cooccurrences-computer/plugin-panels-1-en.png[align="center", title="Which entities should be the nodes?"]
+{nbsp} +
+
+//ST: !
+What does this panel mean?
+
+If you look back at the Excel file, you see that we have "Clients" and their "Purchases".
+
+-> This means we can build 2 different types of networks, depending on our needs:
+
+//ST: !
+
+1. A network showing clients and products, with relations representing purchases from a client to a product.
+
+[graphviz, client-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ client -- product [ label="purchased" ]
+}
+----
+
+To create this kind of networks, choose "Client" in the upper window, and "Purchases" in the lower window of the plugin screen.
+
+
+//ST: !
+[start=2]
+2. Or a network where 2 products are connected, if one client puchased them together.
+
+[graphviz, product-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased together"]
+ a [label="product 1"];
+ b [label="product 2"];
+}
+----
+
+To create this kind of networks, choose "Purchases" in the upper [underline]#and# lower windows of the plugin screen.
+
+
+//ST: !
+==== 4th panel
+//ST: !
+
+image::en/cooccurrences-computer/subfield-delimiter-en.png[align="center", title="Choosing which delimiter is used"]
+{nbsp} +
+
+//ST: !
+
+This 3rd panel asks: in our Excel file, how are different items separated in a given cell?
+In our example, we have used commas: the lists of products purchased are comma-separated:
+
+image::commas-shown-in-red.png[align="center", title="commas shown in red"]
+{nbsp} +
+
+
+//ST: !
+==== 5th panel
+//ST: !
+
+This panel allows you to specify whether the relations are dynamic in time, or not.
+
+In this case, you need an extra column (column C), where a date is shown. We don't cover this case here.
+
+(read the tutorials on dynamic networks for a starter)
+
+
+//ST: !
+==== 6th panel
+//ST: !
+
+image::en/cooccurrences-computer/panel-6-1-en.png[align="center", title="Options panel"]
+{nbsp} +
+
+//ST: !
+
+ "Create links between Purchases agents and links between Purchase agents"
+
+-> If you chose a Product <--> Product kind of network in panel 3, then of course you are interested in links between products. *Check the box*.
+
+//ST: !
+
+-> But if you chose a Client <--> Product kind of network in panel 3, what you need is less obvious.
+
+Let's take the example of client I, who purchased a table and some chairs:
+
+//ST: !
+
+1. Checking the box will create a network where:
+
+[graphviz, inner-links-included, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+ b -- c [label=" co-purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+1. *Not* checking the box will create a network where:
+
+[graphviz, inner-links-excluded, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+ "Remove duplicates"
+
+-> Check this option if your Excel or csv file has duplicate rows that you'd like to be removed
+
+//ST: !
+
+ "Remove self-loops"
+
+If a Client has purchased tables twice, so that we have "table, table" in a cell: this would create a link from table to table (a *self loop*).
+
+-> Check this option if you'd like self loops to be removed.
+
+//ST: !
+==== 7th panel
+//ST: !
+
+This panel recaps all the settings. Click on finish to create the network.
+
+== The end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_html.md b/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_html.md
new file mode 100644
index 0000000..39a4ee3
--- /dev/null
+++ b/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_html.md
@@ -0,0 +1,275 @@
+= Creating a network from a table of cooccurring items
+Clément Levallois
+2017-02-27
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+//ST: Presentation of the plugin
+
+//ST: !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+//ST: !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, each row describes an "occurrence" (of an event, a purchase, a relation, etc.)
+* In columns A, B, C, D, we have the entities involved: column A for persons, column B for what they bought, etc.
+* Connections will be created between entities, when they appear in the same occurrence (so, when they are on thee same row)
+* Occurrences can have dates, multiple instances of an entity can be listed in a given column.
+
+//ST: !
+==== 1. The input
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="An Excel file"]
+{nbsp} +
+
+
+//ST: !
+==== 2. The output
+//ST: !
+
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="Resulting network"]
+{nbsp} +
+
+== Installing the plugin
+//ST: Installing the plugin
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Opening the plugin
+//ST: Opening the plugin
+//ST: !
+
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+{nbsp} +
+
+== Using the plugin
+//ST: Using the plugin
+
+//ST: !
+==== 2nd panel
+//ST: !
+
+image::Select-a-file.png[align="center", title="Select a file"]
+{nbsp} +
+
+//ST: Is your file with a header?
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-2-en.png[align="center", title="A file without headers"]
+{nbsp} +
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="A file with headers"]
+{nbsp} +
+
+//ST: !
+To describe the next screens of the plugin, we will take the example of *the Excel file just shown*, with headers.
+
+//ST: !
+==== 3rd panel
+//ST: !
+
+image::en/cooccurrences-computer/plugin-panels-1-en.png[align="center", title="Which entities should be the nodes?"]
+{nbsp} +
+
+//ST: !
+What does this panel mean?
+
+If you look back at the Excel file, you see that we have "Clients" and their "Purchases".
+
+-> This means we can build 2 different types of networks, depending on our needs:
+
+//ST: !
+
+1. A network showing clients and products, with relations representing purchases from a client to a product.
+
+[graphviz, client-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ client -- product [ label="purchased" ]
+}
+----
+
+To create this kind of networks, choose "Client" in the upper window, and "Purchases" in the lower window of the plugin screen.
+
+
+//ST: !
+[start=2]
+2. Or a network where 2 products are connected, if one client puchased them together.
+
+[graphviz, product-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased together"]
+ a [label="product 1"];
+ b [label="product 2"];
+}
+----
+
+To create this kind of networks, choose "Purchases" in the upper [underline]#and# lower windows of the plugin screen.
+
+
+//ST: !
+==== 4th panel
+//ST: !
+
+image::en/cooccurrences-computer/subfield-delimiter-en.png[align="center", title="Choosing which delimiter is used"]
+{nbsp} +
+
+//ST: !
+
+This 3rd panel asks: in our Excel file, how are different items separated in a given cell?
+In our example, we have used commas: the lists of products purchased are comma-separated:
+
+image::commas-shown-in-red.png[align="center", title="commas shown in red"]
+{nbsp} +
+
+
+//ST: !
+==== 5th panel
+//ST: !
+
+This panel allows you to specify whether the relations are dynamic in time, or not.
+
+In this case, you need an extra column (column C), where a date is shown. We don't cover this case here.
+
+(read the tutorials on dynamic networks for a starter)
+
+
+//ST: !
+==== 6th panel
+//ST: !
+
+image::en/cooccurrences-computer/panel-6-1-en.png[align="center", title="Options panel"]
+{nbsp} +
+
+//ST: !
+
+ "Create links between Purchases agents and links between Purchase agents"
+
+-> If you chose a Product <--> Product kind of network in panel 3, then of course you are interested in links between products. *Check the box*.
+
+//ST: !
+
+-> But if you chose a Client <--> Product kind of network in panel 3, what you need is less obvious.
+
+Let's take the example of client I, who purchased a table and some chairs:
+
+//ST: !
+
+1. Checking the box will create a network where:
+
+[graphviz, inner-links-included, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+ b -- c [label=" co-purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+1. *Not* checking the box will create a network where:
+
+[graphviz, inner-links-excluded, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+ "Remove duplicates"
+
+-> Check this option if your Excel or csv file has duplicate rows that you'd like to be removed
+
+//ST: !
+
+ "Remove self-loops"
+
+If a Client has purchased tables twice, so that we have "table, table" in a cell: this would create a link from table to table (a *self loop*).
+
+-> Check this option if you'd like self loops to be removed.
+
+//ST: !
+==== 7th panel
+//ST: !
+
+This panel recaps all the settings. Click on finish to create the network.
+
+== The end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_pdf.md b/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_pdf.md
new file mode 100644
index 0000000..fc76b9e
--- /dev/null
+++ b/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_pdf.md
@@ -0,0 +1,258 @@
+= Creating a network from a table of cooccurring items
+Clément Levallois
+2017-02-27
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+//ST: Presentation of the plugin
+
+//ST: !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+//ST: !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, each row describes an "occurrence" (of an event, a purchase, a relation, etc.)
+* In columns A, B, C, D, we have the entities involved: column A for persons, column B for what they bought, etc.
+* Connections will be created between entities, when they appear in the same occurrence (so, when they are on thee same row)
+* Occurrences can have dates, multiple instances of an entity can be listed in a given column.
+
+//ST: !
+==== 1. The input
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="An Excel file"]
+{nbsp} +
+
+
+//ST: !
+==== 2. The output
+//ST: !
+
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="Resulting network"]
+{nbsp} +
+
+== Installing the plugin
+//ST: Installing the plugin
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Opening the plugin
+//ST: Opening the plugin
+//ST: !
+
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+{nbsp} +
+
+== Using the plugin
+//ST: Using the plugin
+
+//ST: !
+==== 2nd panel
+//ST: !
+
+image::Select-a-file.png[align="center", title="Select a file"]
+{nbsp} +
+
+//ST: Is your file with a header?
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-2-en.png[align="center", title="A file without headers"]
+{nbsp} +
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="A file with headers"]
+{nbsp} +
+
+//ST: !
+To describe the next screens of the plugin, we will take the example of *the Excel file just shown*, with headers.
+
+//ST: !
+==== 3rd panel
+//ST: !
+
+image::en/cooccurrences-computer/plugin-panels-1-en.png[align="center", title="Which entities should be the nodes?"]
+{nbsp} +
+
+//ST: !
+What does this panel mean?
+
+If you look back at the Excel file, you see that we have "Clients" and their "Purchases".
+
+-> This means we can build 2 different types of networks, depending on our needs:
+
+//ST: !
+
+1. A network showing clients and products, with relations representing purchases from a client to a product.
+
+[graphviz, client-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ client -- product [ label="purchased" ]
+}
+----
+
+To create this kind of networks, choose "Client" in the upper window, and "Purchases" in the lower window of the plugin screen.
+
+
+//ST: !
+[start=2]
+2. Or a network where 2 products are connected, if one client puchased them together.
+
+[graphviz, product-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased together"]
+ a [label="product 1"];
+ b [label="product 2"];
+}
+----
+
+To create this kind of networks, choose "Purchases" in the upper [underline]#and# lower windows of the plugin screen.
+
+
+//ST: !
+==== 4th panel
+//ST: !
+
+image::en/cooccurrences-computer/subfield-delimiter-en.png[align="center", title="Choosing which delimiter is used"]
+{nbsp} +
+
+//ST: !
+
+This 3rd panel asks: in our Excel file, how are different items separated in a given cell?
+In our example, we have used commas: the lists of products purchased are comma-separated:
+
+image::commas-shown-in-red.png[align="center", title="commas shown in red"]
+{nbsp} +
+
+
+//ST: !
+==== 5th panel
+//ST: !
+
+This panel allows you to specify whether the relations are dynamic in time, or not.
+
+In this case, you need an extra column (column C), where a date is shown. We don't cover this case here.
+
+(read the tutorials on dynamic networks for a starter)
+
+
+//ST: !
+==== 6th panel
+//ST: !
+
+image::en/cooccurrences-computer/panel-6-1-en.png[align="center", title="Options panel"]
+{nbsp} +
+
+//ST: !
+
+ "Create links between Purchases agents and links between Purchase agents"
+
+-> If you chose a Product <--> Product kind of network in panel 3, then of course you are interested in links between products. *Check the box*.
+
+//ST: !
+
+-> But if you chose a Client <--> Product kind of network in panel 3, what you need is less obvious.
+
+Let's take the example of client I, who purchased a table and some chairs:
+
+//ST: !
+
+1. Checking the box will create a network where:
+
+[graphviz, inner-links-included, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+ b -- c [label=" co-purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+1. *Not* checking the box will create a network where:
+
+[graphviz, inner-links-excluded, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+ "Remove duplicates"
+
+-> Check this option if your Excel or csv file has duplicate rows that you'd like to be removed
+
+//ST: !
+
+ "Remove self-loops"
+
+If a Client has purchased tables twice, so that we have "table, table" in a cell: this would create a link from table to table (a *self loop*).
+
+-> Check this option if you'd like self loops to be removed.
+
+//ST: !
+==== 7th panel
+//ST: !
+
+This panel recaps all the settings. Click on finish to create the network.
+
+== The end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_slides.md b/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_slides.md
new file mode 100644
index 0000000..a4aa87b
--- /dev/null
+++ b/docs/generated-pdf/subdir/cooccurrences-computer-en_temp_slides.md
@@ -0,0 +1,283 @@
+= Creating a network from a table of cooccurring items
+Clément Levallois
+2017-02-27
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+
+== !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+== !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, each row describes an "occurrence" (of an event, a purchase, a relation, etc.)
+* In columns A, B, C, D, we have the entities involved: column A for persons, column B for what they bought, etc.
+* Connections will be created between entities, when they appear in the same occurrence (so, when they are on thee same row)
+* Occurrences can have dates, multiple instances of an entity can be listed in a given column.
+
+== !
+==== 1. The input
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="An Excel file"]
+
+
+
+== !
+==== 2. The output
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="Resulting network"]
+
+
+== Installing the plugin
+== !
+
+[.stretch]
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+
+
+== !
+
+[.stretch]
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+
+
+== !
+
+[.stretch]
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+
+
+== Opening the plugin
+== !
+
+[.stretch]
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+
+
+== Using the plugin
+
+== !
+==== 2nd panel
+== !
+
+[.stretch]
+image::Select-a-file.png[align="center", title="Select a file"]
+
+
+== Is your file with a header?
+
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/excel-2-en.png[align="center", title="A file without headers"]
+
+
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="A file with headers"]
+
+
+== !
+To describe the next screens of the plugin, we will take the example of *the Excel file just shown*, with headers.
+
+== !
+==== 3rd panel
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/plugin-panels-1-en.png[align="center", title="Which entities should be the nodes?"]
+
+
+== !
+What does this panel mean?
+
+If you look back at the Excel file, you see that we have "Clients" and their "Purchases".
+
+-> This means we can build 2 different types of networks, depending on our needs:
+
+== !
+
+1. A network showing clients and products, with relations representing purchases from a client to a product.
+
+[graphviz, client-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ client -- product [ label="purchased" ]
+}
+----
+
+To create this kind of networks, choose "Client" in the upper window, and "Purchases" in the lower window of the plugin screen.
+
+
+== !
+[start=2]
+2. Or a network where 2 products are connected, if one client puchased them together.
+
+[graphviz, product-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased together"]
+ a [label="product 1"];
+ b [label="product 2"];
+}
+----
+
+To create this kind of networks, choose "Purchases" in the upper [underline]#and# lower windows of the plugin screen.
+
+
+== !
+==== 4th panel
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/subfield-delimiter-en.png[align="center", title="Choosing which delimiter is used"]
+
+
+== !
+
+This 3rd panel asks: in our Excel file, how are different items separated in a given cell?
+In our example, we have used commas: the lists of products purchased are comma-separated:
+
+[.stretch]
+image::commas-shown-in-red.png[align="center", title="commas shown in red"]
+
+
+
+== !
+==== 5th panel
+== !
+
+This panel allows you to specify whether the relations are dynamic in time, or not.
+
+In this case, you need an extra column (column C), where a date is shown. We don't cover this case here.
+
+(read the tutorials on dynamic networks for a starter)
+
+
+== !
+==== 6th panel
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/panel-6-1-en.png[align="center", title="Options panel"]
+
+
+== !
+
+ "Create links between Purchases agents and links between Purchase agents"
+
+-> If you chose a Product <--> Product kind of network in panel 3, then of course you are interested in links between products. *Check the box*.
+
+== !
+
+-> But if you chose a Client <--> Product kind of network in panel 3, what you need is less obvious.
+
+Let's take the example of client I, who purchased a table and some chairs:
+
+== !
+
+1. Checking the box will create a network where:
+
+[graphviz, inner-links-included, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+ b -- c [label=" co-purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+== !
+
+1. *Not* checking the box will create a network where:
+
+[graphviz, inner-links-excluded, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+== !
+
+ "Remove duplicates"
+
+-> Check this option if your Excel or csv file has duplicate rows that you'd like to be removed
+
+== !
+
+ "Remove self-loops"
+
+If a Client has purchased tables twice, so that we have "table, table" in a cell: this would create a link from table to table (a *self loop*).
+
+-> Check this option if you'd like self loops to be removed.
+
+== !
+==== 7th panel
+== !
+
+This panel recaps all the settings. Click on finish to create the network.
+
+== The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/cooccurrences-computer_temp_common.md b/docs/generated-pdf/subdir/cooccurrences-computer_temp_common.md
new file mode 100644
index 0000000..fc76b9e
--- /dev/null
+++ b/docs/generated-pdf/subdir/cooccurrences-computer_temp_common.md
@@ -0,0 +1,258 @@
+= Creating a network from a table of cooccurring items
+Clément Levallois
+2017-02-27
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+//ST: Presentation of the plugin
+
+//ST: !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+//ST: !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, each row describes an "occurrence" (of an event, a purchase, a relation, etc.)
+* In columns A, B, C, D, we have the entities involved: column A for persons, column B for what they bought, etc.
+* Connections will be created between entities, when they appear in the same occurrence (so, when they are on thee same row)
+* Occurrences can have dates, multiple instances of an entity can be listed in a given column.
+
+//ST: !
+==== 1. The input
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="An Excel file"]
+{nbsp} +
+
+
+//ST: !
+==== 2. The output
+//ST: !
+
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="Resulting network"]
+{nbsp} +
+
+== Installing the plugin
+//ST: Installing the plugin
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Opening the plugin
+//ST: Opening the plugin
+//ST: !
+
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+{nbsp} +
+
+== Using the plugin
+//ST: Using the plugin
+
+//ST: !
+==== 2nd panel
+//ST: !
+
+image::Select-a-file.png[align="center", title="Select a file"]
+{nbsp} +
+
+//ST: Is your file with a header?
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-2-en.png[align="center", title="A file without headers"]
+{nbsp} +
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="A file with headers"]
+{nbsp} +
+
+//ST: !
+To describe the next screens of the plugin, we will take the example of *the Excel file just shown*, with headers.
+
+//ST: !
+==== 3rd panel
+//ST: !
+
+image::en/cooccurrences-computer/plugin-panels-1-en.png[align="center", title="Which entities should be the nodes?"]
+{nbsp} +
+
+//ST: !
+What does this panel mean?
+
+If you look back at the Excel file, you see that we have "Clients" and their "Purchases".
+
+-> This means we can build 2 different types of networks, depending on our needs:
+
+//ST: !
+
+1. A network showing clients and products, with relations representing purchases from a client to a product.
+
+[graphviz, client-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ client -- product [ label="purchased" ]
+}
+----
+
+To create this kind of networks, choose "Client" in the upper window, and "Purchases" in the lower window of the plugin screen.
+
+
+//ST: !
+[start=2]
+2. Or a network where 2 products are connected, if one client puchased them together.
+
+[graphviz, product-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased together"]
+ a [label="product 1"];
+ b [label="product 2"];
+}
+----
+
+To create this kind of networks, choose "Purchases" in the upper [underline]#and# lower windows of the plugin screen.
+
+
+//ST: !
+==== 4th panel
+//ST: !
+
+image::en/cooccurrences-computer/subfield-delimiter-en.png[align="center", title="Choosing which delimiter is used"]
+{nbsp} +
+
+//ST: !
+
+This 3rd panel asks: in our Excel file, how are different items separated in a given cell?
+In our example, we have used commas: the lists of products purchased are comma-separated:
+
+image::commas-shown-in-red.png[align="center", title="commas shown in red"]
+{nbsp} +
+
+
+//ST: !
+==== 5th panel
+//ST: !
+
+This panel allows you to specify whether the relations are dynamic in time, or not.
+
+In this case, you need an extra column (column C), where a date is shown. We don't cover this case here.
+
+(read the tutorials on dynamic networks for a starter)
+
+
+//ST: !
+==== 6th panel
+//ST: !
+
+image::en/cooccurrences-computer/panel-6-1-en.png[align="center", title="Options panel"]
+{nbsp} +
+
+//ST: !
+
+ "Create links between Purchases agents and links between Purchase agents"
+
+-> If you chose a Product <--> Product kind of network in panel 3, then of course you are interested in links between products. *Check the box*.
+
+//ST: !
+
+-> But if you chose a Client <--> Product kind of network in panel 3, what you need is less obvious.
+
+Let's take the example of client I, who purchased a table and some chairs:
+
+//ST: !
+
+1. Checking the box will create a network where:
+
+[graphviz, inner-links-included, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+ b -- c [label=" co-purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+1. *Not* checking the box will create a network where:
+
+[graphviz, inner-links-excluded, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+ "Remove duplicates"
+
+-> Check this option if your Excel or csv file has duplicate rows that you'd like to be removed
+
+//ST: !
+
+ "Remove self-loops"
+
+If a Client has purchased tables twice, so that we have "table, table" in a cell: this would create a link from table to table (a *self loop*).
+
+-> Check this option if you'd like self loops to be removed.
+
+//ST: !
+==== 7th panel
+//ST: !
+
+This panel recaps all the settings. Click on finish to create the network.
+
+== The end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/cooccurrences-computer_temp_html.md b/docs/generated-pdf/subdir/cooccurrences-computer_temp_html.md
new file mode 100644
index 0000000..39a4ee3
--- /dev/null
+++ b/docs/generated-pdf/subdir/cooccurrences-computer_temp_html.md
@@ -0,0 +1,275 @@
+= Creating a network from a table of cooccurring items
+Clément Levallois
+2017-02-27
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+//ST: Presentation of the plugin
+
+//ST: !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+//ST: !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, each row describes an "occurrence" (of an event, a purchase, a relation, etc.)
+* In columns A, B, C, D, we have the entities involved: column A for persons, column B for what they bought, etc.
+* Connections will be created between entities, when they appear in the same occurrence (so, when they are on thee same row)
+* Occurrences can have dates, multiple instances of an entity can be listed in a given column.
+
+//ST: !
+==== 1. The input
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="An Excel file"]
+{nbsp} +
+
+
+//ST: !
+==== 2. The output
+//ST: !
+
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="Resulting network"]
+{nbsp} +
+
+== Installing the plugin
+//ST: Installing the plugin
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Opening the plugin
+//ST: Opening the plugin
+//ST: !
+
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+{nbsp} +
+
+== Using the plugin
+//ST: Using the plugin
+
+//ST: !
+==== 2nd panel
+//ST: !
+
+image::Select-a-file.png[align="center", title="Select a file"]
+{nbsp} +
+
+//ST: Is your file with a header?
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-2-en.png[align="center", title="A file without headers"]
+{nbsp} +
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="A file with headers"]
+{nbsp} +
+
+//ST: !
+To describe the next screens of the plugin, we will take the example of *the Excel file just shown*, with headers.
+
+//ST: !
+==== 3rd panel
+//ST: !
+
+image::en/cooccurrences-computer/plugin-panels-1-en.png[align="center", title="Which entities should be the nodes?"]
+{nbsp} +
+
+//ST: !
+What does this panel mean?
+
+If you look back at the Excel file, you see that we have "Clients" and their "Purchases".
+
+-> This means we can build 2 different types of networks, depending on our needs:
+
+//ST: !
+
+1. A network showing clients and products, with relations representing purchases from a client to a product.
+
+[graphviz, client-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ client -- product [ label="purchased" ]
+}
+----
+
+To create this kind of networks, choose "Client" in the upper window, and "Purchases" in the lower window of the plugin screen.
+
+
+//ST: !
+[start=2]
+2. Or a network where 2 products are connected, if one client puchased them together.
+
+[graphviz, product-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased together"]
+ a [label="product 1"];
+ b [label="product 2"];
+}
+----
+
+To create this kind of networks, choose "Purchases" in the upper [underline]#and# lower windows of the plugin screen.
+
+
+//ST: !
+==== 4th panel
+//ST: !
+
+image::en/cooccurrences-computer/subfield-delimiter-en.png[align="center", title="Choosing which delimiter is used"]
+{nbsp} +
+
+//ST: !
+
+This 3rd panel asks: in our Excel file, how are different items separated in a given cell?
+In our example, we have used commas: the lists of products purchased are comma-separated:
+
+image::commas-shown-in-red.png[align="center", title="commas shown in red"]
+{nbsp} +
+
+
+//ST: !
+==== 5th panel
+//ST: !
+
+This panel allows you to specify whether the relations are dynamic in time, or not.
+
+In this case, you need an extra column (column C), where a date is shown. We don't cover this case here.
+
+(read the tutorials on dynamic networks for a starter)
+
+
+//ST: !
+==== 6th panel
+//ST: !
+
+image::en/cooccurrences-computer/panel-6-1-en.png[align="center", title="Options panel"]
+{nbsp} +
+
+//ST: !
+
+ "Create links between Purchases agents and links between Purchase agents"
+
+-> If you chose a Product <--> Product kind of network in panel 3, then of course you are interested in links between products. *Check the box*.
+
+//ST: !
+
+-> But if you chose a Client <--> Product kind of network in panel 3, what you need is less obvious.
+
+Let's take the example of client I, who purchased a table and some chairs:
+
+//ST: !
+
+1. Checking the box will create a network where:
+
+[graphviz, inner-links-included, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+ b -- c [label=" co-purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+1. *Not* checking the box will create a network where:
+
+[graphviz, inner-links-excluded, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+ "Remove duplicates"
+
+-> Check this option if your Excel or csv file has duplicate rows that you'd like to be removed
+
+//ST: !
+
+ "Remove self-loops"
+
+If a Client has purchased tables twice, so that we have "table, table" in a cell: this would create a link from table to table (a *self loop*).
+
+-> Check this option if you'd like self loops to be removed.
+
+//ST: !
+==== 7th panel
+//ST: !
+
+This panel recaps all the settings. Click on finish to create the network.
+
+== The end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/cooccurrences-computer_temp_pdf.md b/docs/generated-pdf/subdir/cooccurrences-computer_temp_pdf.md
new file mode 100644
index 0000000..fc76b9e
--- /dev/null
+++ b/docs/generated-pdf/subdir/cooccurrences-computer_temp_pdf.md
@@ -0,0 +1,258 @@
+= Creating a network from a table of cooccurring items
+Clément Levallois
+2017-02-27
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+//ST: Presentation of the plugin
+
+//ST: !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+//ST: !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, each row describes an "occurrence" (of an event, a purchase, a relation, etc.)
+* In columns A, B, C, D, we have the entities involved: column A for persons, column B for what they bought, etc.
+* Connections will be created between entities, when they appear in the same occurrence (so, when they are on thee same row)
+* Occurrences can have dates, multiple instances of an entity can be listed in a given column.
+
+//ST: !
+==== 1. The input
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="An Excel file"]
+{nbsp} +
+
+
+//ST: !
+==== 2. The output
+//ST: !
+
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="Resulting network"]
+{nbsp} +
+
+== Installing the plugin
+//ST: Installing the plugin
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Opening the plugin
+//ST: Opening the plugin
+//ST: !
+
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+{nbsp} +
+
+== Using the plugin
+//ST: Using the plugin
+
+//ST: !
+==== 2nd panel
+//ST: !
+
+image::Select-a-file.png[align="center", title="Select a file"]
+{nbsp} +
+
+//ST: Is your file with a header?
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-2-en.png[align="center", title="A file without headers"]
+{nbsp} +
+
+//ST: !
+
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="A file with headers"]
+{nbsp} +
+
+//ST: !
+To describe the next screens of the plugin, we will take the example of *the Excel file just shown*, with headers.
+
+//ST: !
+==== 3rd panel
+//ST: !
+
+image::en/cooccurrences-computer/plugin-panels-1-en.png[align="center", title="Which entities should be the nodes?"]
+{nbsp} +
+
+//ST: !
+What does this panel mean?
+
+If you look back at the Excel file, you see that we have "Clients" and their "Purchases".
+
+-> This means we can build 2 different types of networks, depending on our needs:
+
+//ST: !
+
+1. A network showing clients and products, with relations representing purchases from a client to a product.
+
+[graphviz, client-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ client -- product [ label="purchased" ]
+}
+----
+
+To create this kind of networks, choose "Client" in the upper window, and "Purchases" in the lower window of the plugin screen.
+
+
+//ST: !
+[start=2]
+2. Or a network where 2 products are connected, if one client puchased them together.
+
+[graphviz, product-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased together"]
+ a [label="product 1"];
+ b [label="product 2"];
+}
+----
+
+To create this kind of networks, choose "Purchases" in the upper [underline]#and# lower windows of the plugin screen.
+
+
+//ST: !
+==== 4th panel
+//ST: !
+
+image::en/cooccurrences-computer/subfield-delimiter-en.png[align="center", title="Choosing which delimiter is used"]
+{nbsp} +
+
+//ST: !
+
+This 3rd panel asks: in our Excel file, how are different items separated in a given cell?
+In our example, we have used commas: the lists of products purchased are comma-separated:
+
+image::commas-shown-in-red.png[align="center", title="commas shown in red"]
+{nbsp} +
+
+
+//ST: !
+==== 5th panel
+//ST: !
+
+This panel allows you to specify whether the relations are dynamic in time, or not.
+
+In this case, you need an extra column (column C), where a date is shown. We don't cover this case here.
+
+(read the tutorials on dynamic networks for a starter)
+
+
+//ST: !
+==== 6th panel
+//ST: !
+
+image::en/cooccurrences-computer/panel-6-1-en.png[align="center", title="Options panel"]
+{nbsp} +
+
+//ST: !
+
+ "Create links between Purchases agents and links between Purchase agents"
+
+-> If you chose a Product <--> Product kind of network in panel 3, then of course you are interested in links between products. *Check the box*.
+
+//ST: !
+
+-> But if you chose a Client <--> Product kind of network in panel 3, what you need is less obvious.
+
+Let's take the example of client I, who purchased a table and some chairs:
+
+//ST: !
+
+1. Checking the box will create a network where:
+
+[graphviz, inner-links-included, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+ b -- c [label=" co-purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+1. *Not* checking the box will create a network where:
+
+[graphviz, inner-links-excluded, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+//ST: !
+
+ "Remove duplicates"
+
+-> Check this option if your Excel or csv file has duplicate rows that you'd like to be removed
+
+//ST: !
+
+ "Remove self-loops"
+
+If a Client has purchased tables twice, so that we have "table, table" in a cell: this would create a link from table to table (a *self loop*).
+
+-> Check this option if you'd like self loops to be removed.
+
+//ST: !
+==== 7th panel
+//ST: !
+
+This panel recaps all the settings. Click on finish to create the network.
+
+== The end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/cooccurrences-computer_temp_slides.md b/docs/generated-pdf/subdir/cooccurrences-computer_temp_slides.md
new file mode 100644
index 0000000..a4aa87b
--- /dev/null
+++ b/docs/generated-pdf/subdir/cooccurrences-computer_temp_slides.md
@@ -0,0 +1,283 @@
+= Creating a network from a table of cooccurring items
+Clément Levallois
+2017-02-27
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+
+== !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+== !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, each row describes an "occurrence" (of an event, a purchase, a relation, etc.)
+* In columns A, B, C, D, we have the entities involved: column A for persons, column B for what they bought, etc.
+* Connections will be created between entities, when they appear in the same occurrence (so, when they are on thee same row)
+* Occurrences can have dates, multiple instances of an entity can be listed in a given column.
+
+== !
+==== 1. The input
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="An Excel file"]
+
+
+
+== !
+==== 2. The output
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="Resulting network"]
+
+
+== Installing the plugin
+== !
+
+[.stretch]
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+
+
+== !
+
+[.stretch]
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+
+
+== !
+
+[.stretch]
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+
+
+== Opening the plugin
+== !
+
+[.stretch]
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+
+
+== Using the plugin
+
+== !
+==== 2nd panel
+== !
+
+[.stretch]
+image::Select-a-file.png[align="center", title="Select a file"]
+
+
+== Is your file with a header?
+
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/excel-2-en.png[align="center", title="A file without headers"]
+
+
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/excel-1-en.png[align="center", title="A file with headers"]
+
+
+== !
+To describe the next screens of the plugin, we will take the example of *the Excel file just shown*, with headers.
+
+== !
+==== 3rd panel
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/plugin-panels-1-en.png[align="center", title="Which entities should be the nodes?"]
+
+
+== !
+What does this panel mean?
+
+If you look back at the Excel file, you see that we have "Clients" and their "Purchases".
+
+-> This means we can build 2 different types of networks, depending on our needs:
+
+== !
+
+1. A network showing clients and products, with relations representing purchases from a client to a product.
+
+[graphviz, client-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ client -- product [ label="purchased" ]
+}
+----
+
+To create this kind of networks, choose "Client" in the upper window, and "Purchases" in the lower window of the plugin screen.
+
+
+== !
+[start=2]
+2. Or a network where 2 products are connected, if one client puchased them together.
+
+[graphviz, product-to-product, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased together"]
+ a [label="product 1"];
+ b [label="product 2"];
+}
+----
+
+To create this kind of networks, choose "Purchases" in the upper [underline]#and# lower windows of the plugin screen.
+
+
+== !
+==== 4th panel
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/subfield-delimiter-en.png[align="center", title="Choosing which delimiter is used"]
+
+
+== !
+
+This 3rd panel asks: in our Excel file, how are different items separated in a given cell?
+In our example, we have used commas: the lists of products purchased are comma-separated:
+
+[.stretch]
+image::commas-shown-in-red.png[align="center", title="commas shown in red"]
+
+
+
+== !
+==== 5th panel
+== !
+
+This panel allows you to specify whether the relations are dynamic in time, or not.
+
+In this case, you need an extra column (column C), where a date is shown. We don't cover this case here.
+
+(read the tutorials on dynamic networks for a starter)
+
+
+== !
+==== 6th panel
+== !
+
+[.stretch]
+image::en/cooccurrences-computer/panel-6-1-en.png[align="center", title="Options panel"]
+
+
+== !
+
+ "Create links between Purchases agents and links between Purchase agents"
+
+-> If you chose a Product <--> Product kind of network in panel 3, then of course you are interested in links between products. *Check the box*.
+
+== !
+
+-> But if you chose a Client <--> Product kind of network in panel 3, what you need is less obvious.
+
+Let's take the example of client I, who purchased a table and some chairs:
+
+== !
+
+1. Checking the box will create a network where:
+
+[graphviz, inner-links-included, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+ b -- c [label=" co-purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+== !
+
+1. *Not* checking the box will create a network where:
+
+[graphviz, inner-links-excluded, png]
+----
+graph g {
+ rankdir="LR";
+ a -- b [label=" purchased"]
+ a -- c [label=" purchased"]
+
+ a [label="client I"];
+ b [label="table"];
+ c [label="chairs"];
+
+}
+----
+
+== !
+
+ "Remove duplicates"
+
+-> Check this option if your Excel or csv file has duplicate rows that you'd like to be removed
+
+== !
+
+ "Remove self-loops"
+
+If a Client has purchased tables twice, so that we have "table, table" in a cell: this would create a link from table to table (a *self loop*).
+
+-> Check this option if you'd like self loops to be removed.
+
+== !
+==== 7th panel
+== !
+
+This panel recaps all the settings. Click on finish to create the network.
+
+== The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_common.md b/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_common.md
new file mode 100644
index 0000000..ce84d14
--- /dev/null
+++ b/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_common.md
@@ -0,0 +1,211 @@
+= Creating a simple dynamic network
+Clément Levallois
+2017-02-16
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:experimental:
+:sourcedir: ../../../../main/java
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Goals of this tutorial
+//ST: Goals of this tutorial
+
+//ST: !
+
+- We are going to create one very simple *non dynamic* network (network "A").
+- then we will create a modified version of it (network "B").
+- in both files, we need to modify a single line.
+- finally, we will merge networks A and B, forming a new *dynamic* network evolving from version A to B.
+
+//ST: !
+[NOTE]
+====
+The creation of the 2 networks is not a mandatory step: you can use your own network files, as long as they are in gexf file format. In this case, <>.
+
+We create the networks in this tutorial to provide the most simple and clear case.
+====
+
+== open a new project in Gephi
+//ST: open a new project in Gephi
+
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `New Project`
+
+image::Open-a-new-project.png[align="center", title="Open a new project"]
+{nbsp} +
+
+== creating network "A"
+//ST: creating network "A"
+
+//ST: !
+Our project is empty.
+We can use a simple click-and-point tool to create a network:
+
+image::Icon-for-node-creation.png[align="center", title="Icon for node creation"]
+{nbsp} +
+
+//ST: !
+
+image::A-few-nodes-have-been-created.png[align="center", title="A few nodes have been created"]
+{nbsp} +
+
+//ST: !
+When you have created a couple of nodes, click again on the pencil icon to stop the node creation.
+
+Export the network as a file and call it "network A":
+
+//ST: !
+
+image::Exporting-the-network.png[align="center", title="Exporting the network"]
+{nbsp} +
+
+//ST: !
+
+image::Choosing-the-gexf-file-format.png[align="center", title="Choosing the gexf file format"]
+{nbsp} +
+
+//ST: !
+Clicking on "Save" saves the file in the folder you chose. You are back to the view on your graph.
+
+== creating network "B"
+//ST: creating network "B"
+
+//ST: !
+
+Now, click on the "Edge pencil", just below the "Node pencil we have used (careful, the icons are identical!)
+
+//ST: !
+
+image::Choosing-the-gexf-file-format.png[align="center", title="Choosing the gexf file format"]
+{nbsp} +
+
+Create one edge, or a couple of edges, in this way.
+
+//ST: !
+
+Clicking again on the pencil stops the edge creation by mouse clicks.
+
+Then export the network *in the same folder* where we put the previous file (`File -> Export -> Graph File ...`)
+
+*This time, name the file 'network B.gexf' and remember to choose the `gexf` file format just like we did before.*
+
+== modifying just one line in the files network A.gexf and network B.gexf
+//ST: modifying just one line in the files network A.gexf and network B.gexf
+
+//ST: !
+[[merging]]
+From your computer, open network A.gexf with a text editor (don't use Microsoft Word!):
+
+(here is how to do it on a http://www.dummies.com/computers/macs/how-to-open-and-edit-a-text-file-on-a-mac/[Mac], and on https://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/app_notepad.mspx?mfr=true[Windows]).
+
+//ST: !
+image::The-network-file-opened.png[align="center", title="The network file opened"]
+{nbsp} +
+
+//ST: !
+Delete and replace this line by this one:
+
+//ST: !
+
+[source,xml]
+----
+
+----
+See `timestamp="1"` ? It means this will be the first "time slice" of your dynamic network.
+
+Save the file.
+
+//ST: !
+Repeat the steps for network B.gexf:
+
+- open the file in a text editor,
+- find the line ``
+- replace it by:
+
+[source,xml]
+----
+
+----
+
+(see that we have *`timestamp="2"`* here: this is going to be the second time slice!)
+
+- save the file.
+
+== merging network "A" and "B" into a dynamic network
+//ST: merging network "A" and "B" into a dynamic network
+
+//ST: !
+We will use the 2 files network A.gexf and network B.gexf that we have created.
+
+First, let's close the project we have in Gephi, without saving:
+
+//ST: !
+
+image::Closing-the-current-project.png[align="center", title="Closing the current project"]
+{nbsp} +
+
+//ST: !
+Then click on `File -> Open` and navigate to the folder where network A.gexf and network B.gexf are located:
+
+image::Opening-files.png[align="center", title="Opening files"]
+{nbsp} +
+
+//ST: !
+Here, make sure you select the *2 files at once* (using the key kbd:[Ctrl] on pc or kbd:[{commandkey}] on mac).
+
+image::Opening-2-files-at-once.png[align="center", title="Opening 2 files at once"]
+{nbsp} +
+
+//ST: !
+Opening the files display a report window:
+
+image::Import-report-window.png[align="center", title="Import report window"]
+{nbsp} +
+
+
+== enabling the timeline
+//ST: enabling the timeline
+
+//ST: !
+You should see this button, click on it (if not: something went wrong. Retrace your steps)
+
+image::Enable-the-timeline.png[align="center", title="Enable the timeline"]
+{nbsp} +
+
+//ST: !
+Click and slide the right side border of the timeline *to the left*. You should see the edge disappear:
+
+image::Sliding-the-timeline.png[align="center", title="Sliding the timeline"]
+{nbsp} +
+
+//ST: !
+We created a dynamic network.
+It needs to be animated precisely with the timeline, dynamic attributes should be explored, as well as dynamic layouts.
+
+These features will be explained in the coming tutorials on dynamic networks with Gephi.
+
+== more tutorials on dynamic networks with Gephi
+//ST: more tutorials on dynamic networks with Gephi
+
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-Dynamic-Data[The wiki on gephi.org]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_html.md b/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_html.md
new file mode 100644
index 0000000..dfd5f77
--- /dev/null
+++ b/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_html.md
@@ -0,0 +1,228 @@
+= Creating a simple dynamic network
+Clément Levallois
+2017-02-16
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:experimental:
+:sourcedir: ../../../../main/java
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Goals of this tutorial
+//ST: Goals of this tutorial
+
+//ST: !
+
+- We are going to create one very simple *non dynamic* network (network "A").
+- then we will create a modified version of it (network "B").
+- in both files, we need to modify a single line.
+- finally, we will merge networks A and B, forming a new *dynamic* network evolving from version A to B.
+
+//ST: !
+[NOTE]
+====
+The creation of the 2 networks is not a mandatory step: you can use your own network files, as long as they are in gexf file format. In this case, <>.
+
+We create the networks in this tutorial to provide the most simple and clear case.
+====
+
+== open a new project in Gephi
+//ST: open a new project in Gephi
+
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `New Project`
+
+image::Open-a-new-project.png[align="center", title="Open a new project"]
+{nbsp} +
+
+== creating network "A"
+//ST: creating network "A"
+
+//ST: !
+Our project is empty.
+We can use a simple click-and-point tool to create a network:
+
+image::Icon-for-node-creation.png[align="center", title="Icon for node creation"]
+{nbsp} +
+
+//ST: !
+
+image::A-few-nodes-have-been-created.png[align="center", title="A few nodes have been created"]
+{nbsp} +
+
+//ST: !
+When you have created a couple of nodes, click again on the pencil icon to stop the node creation.
+
+Export the network as a file and call it "network A":
+
+//ST: !
+
+image::Exporting-the-network.png[align="center", title="Exporting the network"]
+{nbsp} +
+
+//ST: !
+
+image::Choosing-the-gexf-file-format.png[align="center", title="Choosing the gexf file format"]
+{nbsp} +
+
+//ST: !
+Clicking on "Save" saves the file in the folder you chose. You are back to the view on your graph.
+
+== creating network "B"
+//ST: creating network "B"
+
+//ST: !
+
+Now, click on the "Edge pencil", just below the "Node pencil we have used (careful, the icons are identical!)
+
+//ST: !
+
+image::Choosing-the-gexf-file-format.png[align="center", title="Choosing the gexf file format"]
+{nbsp} +
+
+Create one edge, or a couple of edges, in this way.
+
+//ST: !
+
+Clicking again on the pencil stops the edge creation by mouse clicks.
+
+Then export the network *in the same folder* where we put the previous file (`File -> Export -> Graph File ...`)
+
+*This time, name the file 'network B.gexf' and remember to choose the `gexf` file format just like we did before.*
+
+== modifying just one line in the files network A.gexf and network B.gexf
+//ST: modifying just one line in the files network A.gexf and network B.gexf
+
+//ST: !
+[[merging]]
+From your computer, open network A.gexf with a text editor (don't use Microsoft Word!):
+
+(here is how to do it on a http://www.dummies.com/computers/macs/how-to-open-and-edit-a-text-file-on-a-mac/[Mac], and on https://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/app_notepad.mspx?mfr=true[Windows]).
+
+//ST: !
+image::The-network-file-opened.png[align="center", title="The network file opened"]
+{nbsp} +
+
+//ST: !
+Delete and replace this line by this one:
+
+//ST: !
+
+[source,xml]
+----
+
+----
+See `timestamp="1"` ? It means this will be the first "time slice" of your dynamic network.
+
+Save the file.
+
+//ST: !
+Repeat the steps for network B.gexf:
+
+- open the file in a text editor,
+- find the line ``
+- replace it by:
+
+[source,xml]
+----
+
+----
+
+(see that we have *`timestamp="2"`* here: this is going to be the second time slice!)
+
+- save the file.
+
+== merging network "A" and "B" into a dynamic network
+//ST: merging network "A" and "B" into a dynamic network
+
+//ST: !
+We will use the 2 files network A.gexf and network B.gexf that we have created.
+
+First, let's close the project we have in Gephi, without saving:
+
+//ST: !
+
+image::Closing-the-current-project.png[align="center", title="Closing the current project"]
+{nbsp} +
+
+//ST: !
+Then click on `File -> Open` and navigate to the folder where network A.gexf and network B.gexf are located:
+
+image::Opening-files.png[align="center", title="Opening files"]
+{nbsp} +
+
+//ST: !
+Here, make sure you select the *2 files at once* (using the key kbd:[Ctrl] on pc or kbd:[{commandkey}] on mac).
+
+image::Opening-2-files-at-once.png[align="center", title="Opening 2 files at once"]
+{nbsp} +
+
+//ST: !
+Opening the files display a report window:
+
+image::Import-report-window.png[align="center", title="Import report window"]
+{nbsp} +
+
+
+== enabling the timeline
+//ST: enabling the timeline
+
+//ST: !
+You should see this button, click on it (if not: something went wrong. Retrace your steps)
+
+image::Enable-the-timeline.png[align="center", title="Enable the timeline"]
+{nbsp} +
+
+//ST: !
+Click and slide the right side border of the timeline *to the left*. You should see the edge disappear:
+
+image::Sliding-the-timeline.png[align="center", title="Sliding the timeline"]
+{nbsp} +
+
+//ST: !
+We created a dynamic network.
+It needs to be animated precisely with the timeline, dynamic attributes should be explored, as well as dynamic layouts.
+
+These features will be explained in the coming tutorials on dynamic networks with Gephi.
+
+== more tutorials on dynamic networks with Gephi
+//ST: more tutorials on dynamic networks with Gephi
+
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-Dynamic-Data[The wiki on gephi.org]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_pdf.md b/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_pdf.md
new file mode 100644
index 0000000..ce84d14
--- /dev/null
+++ b/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_pdf.md
@@ -0,0 +1,211 @@
+= Creating a simple dynamic network
+Clément Levallois
+2017-02-16
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:experimental:
+:sourcedir: ../../../../main/java
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Goals of this tutorial
+//ST: Goals of this tutorial
+
+//ST: !
+
+- We are going to create one very simple *non dynamic* network (network "A").
+- then we will create a modified version of it (network "B").
+- in both files, we need to modify a single line.
+- finally, we will merge networks A and B, forming a new *dynamic* network evolving from version A to B.
+
+//ST: !
+[NOTE]
+====
+The creation of the 2 networks is not a mandatory step: you can use your own network files, as long as they are in gexf file format. In this case, <>.
+
+We create the networks in this tutorial to provide the most simple and clear case.
+====
+
+== open a new project in Gephi
+//ST: open a new project in Gephi
+
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `New Project`
+
+image::Open-a-new-project.png[align="center", title="Open a new project"]
+{nbsp} +
+
+== creating network "A"
+//ST: creating network "A"
+
+//ST: !
+Our project is empty.
+We can use a simple click-and-point tool to create a network:
+
+image::Icon-for-node-creation.png[align="center", title="Icon for node creation"]
+{nbsp} +
+
+//ST: !
+
+image::A-few-nodes-have-been-created.png[align="center", title="A few nodes have been created"]
+{nbsp} +
+
+//ST: !
+When you have created a couple of nodes, click again on the pencil icon to stop the node creation.
+
+Export the network as a file and call it "network A":
+
+//ST: !
+
+image::Exporting-the-network.png[align="center", title="Exporting the network"]
+{nbsp} +
+
+//ST: !
+
+image::Choosing-the-gexf-file-format.png[align="center", title="Choosing the gexf file format"]
+{nbsp} +
+
+//ST: !
+Clicking on "Save" saves the file in the folder you chose. You are back to the view on your graph.
+
+== creating network "B"
+//ST: creating network "B"
+
+//ST: !
+
+Now, click on the "Edge pencil", just below the "Node pencil we have used (careful, the icons are identical!)
+
+//ST: !
+
+image::Choosing-the-gexf-file-format.png[align="center", title="Choosing the gexf file format"]
+{nbsp} +
+
+Create one edge, or a couple of edges, in this way.
+
+//ST: !
+
+Clicking again on the pencil stops the edge creation by mouse clicks.
+
+Then export the network *in the same folder* where we put the previous file (`File -> Export -> Graph File ...`)
+
+*This time, name the file 'network B.gexf' and remember to choose the `gexf` file format just like we did before.*
+
+== modifying just one line in the files network A.gexf and network B.gexf
+//ST: modifying just one line in the files network A.gexf and network B.gexf
+
+//ST: !
+[[merging]]
+From your computer, open network A.gexf with a text editor (don't use Microsoft Word!):
+
+(here is how to do it on a http://www.dummies.com/computers/macs/how-to-open-and-edit-a-text-file-on-a-mac/[Mac], and on https://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/app_notepad.mspx?mfr=true[Windows]).
+
+//ST: !
+image::The-network-file-opened.png[align="center", title="The network file opened"]
+{nbsp} +
+
+//ST: !
+Delete and replace this line by this one:
+
+//ST: !
+
+[source,xml]
+----
+
+----
+See `timestamp="1"` ? It means this will be the first "time slice" of your dynamic network.
+
+Save the file.
+
+//ST: !
+Repeat the steps for network B.gexf:
+
+- open the file in a text editor,
+- find the line ``
+- replace it by:
+
+[source,xml]
+----
+
+----
+
+(see that we have *`timestamp="2"`* here: this is going to be the second time slice!)
+
+- save the file.
+
+== merging network "A" and "B" into a dynamic network
+//ST: merging network "A" and "B" into a dynamic network
+
+//ST: !
+We will use the 2 files network A.gexf and network B.gexf that we have created.
+
+First, let's close the project we have in Gephi, without saving:
+
+//ST: !
+
+image::Closing-the-current-project.png[align="center", title="Closing the current project"]
+{nbsp} +
+
+//ST: !
+Then click on `File -> Open` and navigate to the folder where network A.gexf and network B.gexf are located:
+
+image::Opening-files.png[align="center", title="Opening files"]
+{nbsp} +
+
+//ST: !
+Here, make sure you select the *2 files at once* (using the key kbd:[Ctrl] on pc or kbd:[{commandkey}] on mac).
+
+image::Opening-2-files-at-once.png[align="center", title="Opening 2 files at once"]
+{nbsp} +
+
+//ST: !
+Opening the files display a report window:
+
+image::Import-report-window.png[align="center", title="Import report window"]
+{nbsp} +
+
+
+== enabling the timeline
+//ST: enabling the timeline
+
+//ST: !
+You should see this button, click on it (if not: something went wrong. Retrace your steps)
+
+image::Enable-the-timeline.png[align="center", title="Enable the timeline"]
+{nbsp} +
+
+//ST: !
+Click and slide the right side border of the timeline *to the left*. You should see the edge disappear:
+
+image::Sliding-the-timeline.png[align="center", title="Sliding the timeline"]
+{nbsp} +
+
+//ST: !
+We created a dynamic network.
+It needs to be animated precisely with the timeline, dynamic attributes should be explored, as well as dynamic layouts.
+
+These features will be explained in the coming tutorials on dynamic networks with Gephi.
+
+== more tutorials on dynamic networks with Gephi
+//ST: more tutorials on dynamic networks with Gephi
+
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-Dynamic-Data[The wiki on gephi.org]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_slides.md b/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_slides.md
new file mode 100644
index 0000000..df4ed7e
--- /dev/null
+++ b/docs/generated-pdf/subdir/creating-a-simple-dynamic-network_temp_slides.md
@@ -0,0 +1,232 @@
+= Creating a simple dynamic network
+Clément Levallois
+2017-02-16
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:experimental:
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Goals of this tutorial
+
+== !
+
+- We are going to create one very simple *non dynamic* network (network "A").
+- then we will create a modified version of it (network "B").
+- in both files, we need to modify a single line.
+- finally, we will merge networks A and B, forming a new *dynamic* network evolving from version A to B.
+
+== !
+[NOTE]
+====
+The creation of the 2 networks is not a mandatory step: you can use your own network files, as long as they are in gexf file format. In this case, <>.
+
+We create the networks in this tutorial to provide the most simple and clear case.
+====
+
+== open a new project in Gephi
+
+== !
+- open Gephi. On the Welcome screen that appears, click on `New Project`
+
+[.stretch]
+image::Open-a-new-project.png[align="center", title="Open a new project"]
+
+
+== creating network "A"
+
+== !
+Our project is empty.
+We can use a simple click-and-point tool to create a network:
+
+[.stretch]
+image::Icon-for-node-creation.png[align="center", title="Icon for node creation"]
+
+
+== !
+
+[.stretch]
+image::A-few-nodes-have-been-created.png[align="center", title="A few nodes have been created"]
+
+
+== !
+When you have created a couple of nodes, click again on the pencil icon to stop the node creation.
+
+Export the network as a file and call it "network A":
+
+== !
+
+[.stretch]
+image::Exporting-the-network.png[align="center", title="Exporting the network"]
+
+
+== !
+
+[.stretch]
+image::Choosing-the-gexf-file-format.png[align="center", title="Choosing the gexf file format"]
+
+
+== !
+Clicking on "Save" saves the file in the folder you chose. You are back to the view on your graph.
+
+== creating network "B"
+
+== !
+
+Now, click on the "Edge pencil", just below the "Node pencil we have used (careful, the icons are identical!)
+
+== !
+
+[.stretch]
+image::Choosing-the-gexf-file-format.png[align="center", title="Choosing the gexf file format"]
+
+
+Create one edge, or a couple of edges, in this way.
+
+== !
+
+Clicking again on the pencil stops the edge creation by mouse clicks.
+
+Then export the network *in the same folder* where we put the previous file (`File -> Export -> Graph File ...`)
+
+*This time, name the file 'network B.gexf' and remember to choose the `gexf` file format just like we did before.*
+
+== modifying just one line in the files network A.gexf and network B.gexf
+
+== !
+[[merging]]
+From your computer, open network A.gexf with a text editor (don't use Microsoft Word!):
+
+(here is how to do it on a http://www.dummies.com/computers/macs/how-to-open-and-edit-a-text-file-on-a-mac/[Mac], and on https://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/app_notepad.mspx?mfr=true[Windows]).
+
+== !
+[.stretch]
+image::The-network-file-opened.png[align="center", title="The network file opened"]
+
+
+== !
+Delete and replace this line by this one:
+
+== !
+
+[source,xml]
+----
+
+----
+See `timestamp="1"` ? It means this will be the first "time slice" of your dynamic network.
+
+Save the file.
+
+== !
+Repeat the steps for network B.gexf:
+
+- open the file in a text editor,
+- find the line ``
+- replace it by:
+
+[source,xml]
+----
+
+----
+
+(see that we have *`timestamp="2"`* here: this is going to be the second time slice!)
+
+- save the file.
+
+== merging network "A" and "B" into a dynamic network
+
+== !
+We will use the 2 files network A.gexf and network B.gexf that we have created.
+
+First, let's close the project we have in Gephi, without saving:
+
+== !
+
+[.stretch]
+image::Closing-the-current-project.png[align="center", title="Closing the current project"]
+
+
+== !
+Then click on `File -> Open` and navigate to the folder where network A.gexf and network B.gexf are located:
+
+[.stretch]
+image::Opening-files.png[align="center", title="Opening files"]
+
+
+== !
+Here, make sure you select the *2 files at once* (using the key kbd:[Ctrl] on pc or kbd:[{commandkey}] on mac).
+
+[.stretch]
+image::Opening-2-files-at-once.png[align="center", title="Opening 2 files at once"]
+
+
+== !
+Opening the files display a report window:
+
+[.stretch]
+image::Import-report-window.png[align="center", title="Import report window"]
+
+
+
+== enabling the timeline
+
+== !
+You should see this button, click on it (if not: something went wrong. Retrace your steps)
+
+[.stretch]
+image::Enable-the-timeline.png[align="center", title="Enable the timeline"]
+
+
+== !
+Click and slide the right side border of the timeline *to the left*. You should see the edge disappear:
+
+[.stretch]
+image::Sliding-the-timeline.png[align="center", title="Sliding the timeline"]
+
+
+== !
+We created a dynamic network.
+It needs to be animated precisely with the timeline, dynamic attributes should be explored, as well as dynamic layouts.
+
+These features will be explained in the coming tutorials on dynamic networks with Gephi.
+
+== more tutorials on dynamic networks with Gephi
+
+== !
+
+- https://github.com/gephi/gephi/wiki/Import-Dynamic-Data[The wiki on gephi.org]
+
+
+== The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/gephi-for-developers-en_temp_common.md b/docs/generated-pdf/subdir/gephi-for-developers-en_temp_common.md
new file mode 100644
index 0000000..e69de29
diff --git a/docs/generated-pdf/subdir/gephi-for-developers-en_temp_html.md b/docs/generated-pdf/subdir/gephi-for-developers-en_temp_html.md
new file mode 100644
index 0000000..289229b
--- /dev/null
+++ b/docs/generated-pdf/subdir/gephi-for-developers-en_temp_html.md
@@ -0,0 +1,17 @@
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/gephi-for-developers-en_temp_pdf.md b/docs/generated-pdf/subdir/gephi-for-developers-en_temp_pdf.md
new file mode 100644
index 0000000..e69de29
diff --git a/docs/generated-pdf/subdir/gephi-for-developers-en_temp_slides.md b/docs/generated-pdf/subdir/gephi-for-developers-en_temp_slides.md
new file mode 100644
index 0000000..289229b
--- /dev/null
+++ b/docs/generated-pdf/subdir/gephi-for-developers-en_temp_slides.md
@@ -0,0 +1,17 @@
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/history-en_temp_common.md b/docs/generated-pdf/subdir/history-en_temp_common.md
new file mode 100644
index 0000000..0f3cd81
--- /dev/null
+++ b/docs/generated-pdf/subdir/history-en_temp_common.md
@@ -0,0 +1,76 @@
+= History of Gephi
+Clément Levallois
+2017-01-11
+
+last modified: {docdate}
+
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image:gephi-logo-2010-transparent.png[width="450" align="center"]
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== 2006 and Web Atlas
+
+//ST: History of Gephi
+
+The origin of Gephi lays in http://webatlas.fr/wp/[Web Atlas], a project started in 2006 involving Mathieu Jacomy, who was interested in the exploration of web-based graphs.
+This was about the same time http://www.cond.org/[Eytan Adar] was developing his https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjljNmDtbrRAhXL6xQKHWDdDRMQFggcMAA&url=http%3A%2F%2Fgraphexploration.cond.org%2Fchi2006%2Fguess-chi2006.pdf&usg=AFQjCNETmuZqHoaZyJSYpSuTGQ87PNSCLA&sig2=ns0z9wqVRIo5riVtHv1QfQ[GUESS software]
+
+image::matthieu-jacomy.jpg[align="center", title="Mathieu Jacomy", id=package_overview]
+{nbsp} +
+
+//ST: !
+
+In 2007/2008, this project morphed into Gephi, developed by a team of students at Université Technologique de Compiègne (https://www.utc.fr/en.html[UTC]).
+
+image::initial-team.png[width=450, align="center", title="The initial Gephi team"]
+{nbsp} +
+
+== A series of metrics on Gephi's popularity over time
+
+//ST: !
+
+Gephi has evolved in several major milestones:
+
+- version 0.6 (2009): the initial public release. Contains already the filter system.
+- version 0.7 (2010): Major overhaul of the graphics interface. Started using OpenGL framework for graphics acceleration. A system of plugin is created.
+
+//ST: !
+
+- version 0.8 (2011): Introduced dynamic graphs.
+
+2011 to 2016: Many plugins were created for Gephi, and the software sarted becoming much popular.
+
+Mac computer users had issues installing Gephi on their computers because of a Java compatibility issue.
+
+//ST: !
+
+version 0.9 (December 2015):
+
+- A graph engine optimized for memory is introduced: Gephi can handle much larger graphs.
+- The codebase for the project is "mavenized", making the code more modular and easier to maintain.
+- Compatibility issues with Mac OS are solved.
+
+//ST: !
+
+In 2016, Gephi is translated in French, Spanish, Japanese, Russian, Polish, Brazilian Portuguese, Chinese, Czech and German.
+
+//ST: Cumulative downloads for Gephi
+
+pass:[]
+
+
+//ST: General interest for Gephi and related software over 2004 - 2016
+
+pass:[]
+
+
+//ST: Academic citations for Gephi (counting only citations to Bastian et al., 2009)
+
+image::google-scholar-gephi.png[align="center", title=""]
+{nbsp} +
diff --git a/docs/generated-pdf/subdir/history-en_temp_html.md b/docs/generated-pdf/subdir/history-en_temp_html.md
new file mode 100644
index 0000000..0a86abd
--- /dev/null
+++ b/docs/generated-pdf/subdir/history-en_temp_html.md
@@ -0,0 +1,93 @@
+= History of Gephi
+Clément Levallois
+2017-01-11
+
+last modified: {docdate}
+
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image:gephi-logo-2010-transparent.png[width="450" align="center"]
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== 2006 and Web Atlas
+
+//ST: History of Gephi
+
+The origin of Gephi lays in http://webatlas.fr/wp/[Web Atlas], a project started in 2006 involving Mathieu Jacomy, who was interested in the exploration of web-based graphs.
+This was about the same time http://www.cond.org/[Eytan Adar] was developing his https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjljNmDtbrRAhXL6xQKHWDdDRMQFggcMAA&url=http%3A%2F%2Fgraphexploration.cond.org%2Fchi2006%2Fguess-chi2006.pdf&usg=AFQjCNETmuZqHoaZyJSYpSuTGQ87PNSCLA&sig2=ns0z9wqVRIo5riVtHv1QfQ[GUESS software]
+
+image::matthieu-jacomy.jpg[align="center", title="Mathieu Jacomy", id=package_overview]
+{nbsp} +
+
+//ST: !
+
+In 2007/2008, this project morphed into Gephi, developed by a team of students at Université Technologique de Compiègne (https://www.utc.fr/en.html[UTC]).
+
+image::initial-team.png[width=450, align="center", title="The initial Gephi team"]
+{nbsp} +
+
+== A series of metrics on Gephi's popularity over time
+
+//ST: !
+
+Gephi has evolved in several major milestones:
+
+- version 0.6 (2009): the initial public release. Contains already the filter system.
+- version 0.7 (2010): Major overhaul of the graphics interface. Started using OpenGL framework for graphics acceleration. A system of plugin is created.
+
+//ST: !
+
+- version 0.8 (2011): Introduced dynamic graphs.
+
+2011 to 2016: Many plugins were created for Gephi, and the software sarted becoming much popular.
+
+Mac computer users had issues installing Gephi on their computers because of a Java compatibility issue.
+
+//ST: !
+
+version 0.9 (December 2015):
+
+- A graph engine optimized for memory is introduced: Gephi can handle much larger graphs.
+- The codebase for the project is "mavenized", making the code more modular and easier to maintain.
+- Compatibility issues with Mac OS are solved.
+
+//ST: !
+
+In 2016, Gephi is translated in French, Spanish, Japanese, Russian, Polish, Brazilian Portuguese, Chinese, Czech and German.
+
+//ST: Cumulative downloads for Gephi
+
+pass:[]
+
+
+//ST: General interest for Gephi and related software over 2004 - 2016
+
+pass:[]
+
+
+//ST: Academic citations for Gephi (counting only citations to Bastian et al., 2009)
+
+image::google-scholar-gephi.png[align="center", title=""]
+{nbsp} +
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/history-en_temp_pdf.md b/docs/generated-pdf/subdir/history-en_temp_pdf.md
new file mode 100644
index 0000000..4ab0872
--- /dev/null
+++ b/docs/generated-pdf/subdir/history-en_temp_pdf.md
@@ -0,0 +1,78 @@
+= History of Gephi
+Clément Levallois
+2017-01-11
+
+last modified: {docdate}
+
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image:gephi-logo-2010-transparent.png[width="450" align="center"]
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== 2006 and Web Atlas
+
+//ST: History of Gephi
+
+The origin of Gephi lays in http://webatlas.fr/wp/[Web Atlas], a project started in 2006 involving Mathieu Jacomy, who was interested in the exploration of web-based graphs.
+This was about the same time http://www.cond.org/[Eytan Adar] was developing his https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjljNmDtbrRAhXL6xQKHWDdDRMQFggcMAA&url=http%3A%2F%2Fgraphexploration.cond.org%2Fchi2006%2Fguess-chi2006.pdf&usg=AFQjCNETmuZqHoaZyJSYpSuTGQ87PNSCLA&sig2=ns0z9wqVRIo5riVtHv1QfQ[GUESS software]
+
+image::matthieu-jacomy.jpg[align="center", title="Mathieu Jacomy", id=package_overview]
+{nbsp} +
+
+//ST: !
+
+In 2007/2008, this project morphed into Gephi, developed by a team of students at Université Technologique de Compiègne (https://www.utc.fr/en.html[UTC]).
+
+image::initial-team.png[width=450, align="center", title="The initial Gephi team"]
+{nbsp} +
+
+== A series of metrics on Gephi's popularity over time
+
+//ST: !
+
+Gephi has evolved in several major milestones:
+
+- version 0.6 (2009): the initial public release. Contains already the filter system.
+- version 0.7 (2010): Major overhaul of the graphics interface. Started using OpenGL framework for graphics acceleration. A system of plugin is created.
+
+//ST: !
+
+- version 0.8 (2011): Introduced dynamic graphs.
+
+2011 to 2016: Many plugins were created for Gephi, and the software sarted becoming much popular.
+
+Mac computer users had issues installing Gephi on their computers because of a Java compatibility issue.
+
+//ST: !
+
+version 0.9 (December 2015):
+
+- A graph engine optimized for memory is introduced: Gephi can handle much larger graphs.
+- The codebase for the project is "mavenized", making the code more modular and easier to maintain.
+- Compatibility issues with Mac OS are solved.
+
+//ST: !
+
+In 2016, Gephi is translated in French, Spanish, Japanese, Russian, Polish, Brazilian Portuguese, Chinese, Czech and German.
+
+//ST: Cumulative downloads for Gephi
+
+
+image::Gephi--cumulative-downloads.png[align="center", title="Gephi, cumulative downloads"]
+{nbsp} +
+
+//ST: General interest for Gephi and related software over 2004 - 2016
+
+
+image::google-trends-gephi.png[align="center", title=""]
+{nbsp} +
+
+//ST: Academic citations for Gephi (counting only citations to Bastian et al., 2009)
+
+image::google-scholar-gephi.png[align="center", title=""]
+{nbsp} +
diff --git a/docs/generated-pdf/subdir/history-en_temp_slides.md b/docs/generated-pdf/subdir/history-en_temp_slides.md
new file mode 100644
index 0000000..b257c8f
--- /dev/null
+++ b/docs/generated-pdf/subdir/history-en_temp_slides.md
@@ -0,0 +1,93 @@
+= History of Gephi
+Clément Levallois
+2017-01-11
+
+last modified: {docdate}
+
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image:gephi-logo-2010-transparent.png[width="450" align="center"]
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== History of Gephi
+
+The origin of Gephi lays in http://webatlas.fr/wp/[Web Atlas], a project started in 2006 involving Mathieu Jacomy, who was interested in the exploration of web-based graphs.
+This was about the same time http://www.cond.org/[Eytan Adar] was developing his https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjljNmDtbrRAhXL6xQKHWDdDRMQFggcMAA&url=http%3A%2F%2Fgraphexploration.cond.org%2Fchi2006%2Fguess-chi2006.pdf&usg=AFQjCNETmuZqHoaZyJSYpSuTGQ87PNSCLA&sig2=ns0z9wqVRIo5riVtHv1QfQ[GUESS software]
+
+[.stretch]
+image::matthieu-jacomy.jpg[align="center", title="Mathieu Jacomy", id=package_overview]
+
+
+== !
+
+In 2007/2008, this project morphed into Gephi, developed by a team of students at Université Technologique de Compiègne (https://www.utc.fr/en.html[UTC]).
+
+[.stretch]
+image::initial-team.png[width=450, align="center", title="The initial Gephi team"]
+
+
+
+== !
+
+Gephi has evolved in several major milestones:
+
+- version 0.6 (2009): the initial public release. Contains already the filter system.
+- version 0.7 (2010): Major overhaul of the graphics interface. Started using OpenGL framework for graphics acceleration. A system of plugin is created.
+
+== !
+
+- version 0.8 (2011): Introduced dynamic graphs.
+
+2011 to 2016: Many plugins were created for Gephi, and the software sarted becoming much popular.
+
+Mac computer users had issues installing Gephi on their computers because of a Java compatibility issue.
+
+== !
+
+version 0.9 (December 2015):
+
+- A graph engine optimized for memory is introduced: Gephi can handle much larger graphs.
+- The codebase for the project is "mavenized", making the code more modular and easier to maintain.
+- Compatibility issues with Mac OS are solved.
+
+== !
+
+In 2016, Gephi is translated in French, Spanish, Japanese, Russian, Polish, Brazilian Portuguese, Chinese, Czech and German.
+
+== Cumulative downloads for Gephi
+
+pass:[]
+
+
+== General interest for Gephi and related software over 2004 - 2016
+
+pass:[]
+
+
+== Academic citations for Gephi (counting only citations to Bastian et al., 2009)
+
+[.stretch]
+image::google-scholar-gephi.png[align="center", title=""]
+
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_common.md b/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_common.md
new file mode 100644
index 0000000..c0cee86
--- /dev/null
+++ b/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_common.md
@@ -0,0 +1,2 @@
+image::http://i.giphy.com/bkKvvzE9PEcTK.gif[]
+{nbsp} +
diff --git a/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_html.md b/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_html.md
new file mode 100644
index 0000000..84a849e
--- /dev/null
+++ b/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_html.md
@@ -0,0 +1,19 @@
+image::http://i.giphy.com/bkKvvzE9PEcTK.gif[]
+{nbsp} +
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_pdf.md b/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_pdf.md
new file mode 100644
index 0000000..000b574
--- /dev/null
+++ b/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_pdf.md
@@ -0,0 +1,2 @@
+
+{nbsp} +
diff --git a/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_slides.md b/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_slides.md
new file mode 100644
index 0000000..cea8bac
--- /dev/null
+++ b/docs/generated-pdf/subdir/how should Gephi be pronounced_temp_slides.md
@@ -0,0 +1,20 @@
+[.stretch]
+image::http://i.giphy.com/bkKvvzE9PEcTK.gif[]
+
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_common.md b/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_common.md
new file mode 100644
index 0000000..9804103
--- /dev/null
+++ b/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_common.md
@@ -0,0 +1,243 @@
+= Importing data in Gephi
+Clément Levallois
+2017-02-03
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Importing an existing network file
+//ST: Importing an existing network file
+
+//ST: !
+Gephi handles the import of network files in a variety of formats:
+
+image::network-formats-gephi-import-en.png[align="center",title="file formats supported by Gephi. Source: gephi.org"]
+{nbsp} +
+
+//ST: !
+To import any of these files, just choose the `File -> Open` menu
+
+== Importing nodes with labels and their relations: simplest version
+
+//ST: Importing nodes with labels and their relations: simplest version
+
+(looking for the complete, full featured csv import version? <>.)
+
+//ST: !
+The easiest is probably to just have a text file like this one:
+
+----
+Source,Target <1>
+Jeremy,Jennifer
+Valerian,Jeremy
+----
+
+<1> This line is mandatory. Don't put a space between the comma and "Target"
+
+Just write this in a text editor and save it as a file `example.txt`
+
+//ST: !
+
+To import this file in Gephi, go to `File->Import Spreadsheet`. Then in the window:
+
+image::Importing-nodes-with-labels-and-their-relations.png[align="center",title="Importing nodes with labels and their relations"]
+{nbsp} +
+
+//ST: !
+In this window, make sure the box "Create missing nodes" is checked:
+
+image::Second-screen.png[align="center",title="Second screen"]
+{nbsp} +
+
+[[full-csv-version]]
+//ST: Importing nodes with labels and their relations: full version
+
+//ST: !
+Let's look again at the simple text file we used:
+
+....
+Source,Target
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+
+There are 2 issues and several missing features, listed below:
+
+//ST: !
+==== 1. labels containing commas in them will not work.
+
+Imagine our characters have first names and last names separated by a comma:
+
+Dubois, Jeremy
+
+Rodriguez, Valerian
+
+//ST: !
+This will look like:
+
+....
+Source,Target
+Dubois, Jeremy,Jeremy,Jennifer
+Rodriguez, Valerian,Dubois, Jeremy
+....
+
+//ST: !
+
+Note that we have 3 commas per line, instead of one!
+In this mess, Gephi will not detect where the node labels start and end.
+The import will break.
+
+//ST: !
+The proper solution is to put node labels inside *double quotes*:
+
+....
+Source,Target
+"Dubois, Jeremy","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy"
+....
+
+Be careful to use these straight " " double quotes, not the curvy ones: “ ” which French keyboards sometimes have.
+
+//ST: !
+==== 2. labels containing double quotes " " in them will not work.
+
+Example: let's imagine that one of our characters has a middle name: Jeremy "Danger" Dubois.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy "Danger"","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy "Danger""
+....
+
+//ST: !
+These extra " " will make the Gephi import break:
+
+image::en/import-spreadsheet-4-en.png[align="center",title="The import bugs - the middle name has disappeared"]
+{nbsp} +
+
+
+//ST: !
+The solution consists in *adding an extra double quote in front of the double quotes*.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy ""Danger""","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy ""Danger"""
+....
+
+//ST: !
+This time, Gephi imports the network correctly:
+
+image::en/import-spreadsheet-3-en.png[align="center",title="Importing node labels containing double quotes"]
+{nbsp} +
+
+== Importing more than labels: nodes and edges attributes
+//ST: Importing more than labels: nodes and edges attributes
+
+//ST: !
+To import attributes we will need to proceed differently.
+
+We need 2 text files: one for the list of nodes, one for the list of relations (edges)
+
+//ST: !
+
+An example file with a list of nodes:
+....
+Id,Label,Date of Birth,Place of Birth,Years of experience,Rating <1>
+3,"Dubois, Jeremy ""Danger""",17/09/1980,"Paris",8,9.27
+1,"Jeremy,Jennifer",25/03/1978,"Tampa",8,4.34
+45,"Rodriguez, Valerian",30/04/1985,"Berlin",5,6.66
+....
+<1> Nodes *must* have at least an Id and a Label. Don't put spaces after the commas
+
+//ST: !
+An example file with a list of edges:
+....
+Source,Target,Weight,Type,Where first met <1>
+1,45,3,"undirected",London <2>
+....
+<1> Edges *must* have at least a Source and Target. Other fields are optional.
+<2> "undirected", the alternative is "directed". Directed edges have arrow heads.
+
+//ST: !
+Let's import the list of nodes first. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-nodes-with-attributes.png[align="center",title="Importing a list of nodes with attributes"]
+{nbsp} +
+
+//ST: !
+in the next screen, we must be careful with a couple of things:
+
+image::The-attributes-of-the-nodes.png[align="center",title="The attributes of the nodes"]
+{nbsp} +
+
+//ST: !
+Then we can import the file with the list of relations. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-edges-with-attributes.png[align="center",title="Importing a list of edges with attributes"]
+{nbsp} +
+
+//ST: !
+
+image::The-attributes-of-the-edges.png[align="center",title="The attributes of the edges"]
+{nbsp} +
+
+== Memo card
+//ST: Memo card
+
+//ST: !
+Nodes
+
+- header must be at least `Id,Label`
+
+//ST: !
+Edges
+
+- header must be at least `Source,Target`
+- To have an attribute for the "thickness" or "strength" of a relation, this attribute must be called "Weight".
+- Want arrows on your links? Add an attribute "Type", with value "Directed"
+- Don't wan't arrows? Add an attribute "Type", with value "Undirected"
+
+//ST: !
+Types of attributes: which to choose?
+
+image::en/import-spreadsheet-9-en.png[align="center",title="Kinds of attributes"]
+{nbsp} +
+
+//ST: !
+
+- Textual attribute: `String`. Nodes sharing the same textual value can be colored the same, or filtered together...
+- Numerical attribute: `Integer`, `Double` or `Float`. Nodes can be resized according to their value, or colorized in a gradient. Filters can be applied based on the range of values.
+- Boolean attribute: just a `true` or `false` value. Useful for filtering out some nodes which are true or false on some parameter.
+- Other types of attributes: not needed for the moment.
+
+
+== (to be continued)
+//ST: (to be continued)
+
+
+== More tutorials on importing data to Gephi
+//ST: More tutorials on importing data to Gephi
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-CSV-Data[The Gephi wiki on importing csv]
+- https://www.youtube.com/watch?v=3Im7vNRA2ns[Video "How to import a CSV into Gephi" by Jen Golbeck]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_html.md b/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_html.md
new file mode 100644
index 0000000..ea4650d
--- /dev/null
+++ b/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_html.md
@@ -0,0 +1,260 @@
+= Importing data in Gephi
+Clément Levallois
+2017-02-03
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Importing an existing network file
+//ST: Importing an existing network file
+
+//ST: !
+Gephi handles the import of network files in a variety of formats:
+
+image::network-formats-gephi-import-en.png[align="center",title="file formats supported by Gephi. Source: gephi.org"]
+{nbsp} +
+
+//ST: !
+To import any of these files, just choose the `File -> Open` menu
+
+== Importing nodes with labels and their relations: simplest version
+
+//ST: Importing nodes with labels and their relations: simplest version
+
+(looking for the complete, full featured csv import version? <>.)
+
+//ST: !
+The easiest is probably to just have a text file like this one:
+
+----
+Source,Target <1>
+Jeremy,Jennifer
+Valerian,Jeremy
+----
+
+<1> This line is mandatory. Don't put a space between the comma and "Target"
+
+Just write this in a text editor and save it as a file `example.txt`
+
+//ST: !
+
+To import this file in Gephi, go to `File->Import Spreadsheet`. Then in the window:
+
+image::Importing-nodes-with-labels-and-their-relations.png[align="center",title="Importing nodes with labels and their relations"]
+{nbsp} +
+
+//ST: !
+In this window, make sure the box "Create missing nodes" is checked:
+
+image::Second-screen.png[align="center",title="Second screen"]
+{nbsp} +
+
+[[full-csv-version]]
+//ST: Importing nodes with labels and their relations: full version
+
+//ST: !
+Let's look again at the simple text file we used:
+
+....
+Source,Target
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+
+There are 2 issues and several missing features, listed below:
+
+//ST: !
+==== 1. labels containing commas in them will not work.
+
+Imagine our characters have first names and last names separated by a comma:
+
+Dubois, Jeremy
+
+Rodriguez, Valerian
+
+//ST: !
+This will look like:
+
+....
+Source,Target
+Dubois, Jeremy,Jeremy,Jennifer
+Rodriguez, Valerian,Dubois, Jeremy
+....
+
+//ST: !
+
+Note that we have 3 commas per line, instead of one!
+In this mess, Gephi will not detect where the node labels start and end.
+The import will break.
+
+//ST: !
+The proper solution is to put node labels inside *double quotes*:
+
+....
+Source,Target
+"Dubois, Jeremy","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy"
+....
+
+Be careful to use these straight " " double quotes, not the curvy ones: “ ” which French keyboards sometimes have.
+
+//ST: !
+==== 2. labels containing double quotes " " in them will not work.
+
+Example: let's imagine that one of our characters has a middle name: Jeremy "Danger" Dubois.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy "Danger"","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy "Danger""
+....
+
+//ST: !
+These extra " " will make the Gephi import break:
+
+image::en/import-spreadsheet-4-en.png[align="center",title="The import bugs - the middle name has disappeared"]
+{nbsp} +
+
+
+//ST: !
+The solution consists in *adding an extra double quote in front of the double quotes*.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy ""Danger""","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy ""Danger"""
+....
+
+//ST: !
+This time, Gephi imports the network correctly:
+
+image::en/import-spreadsheet-3-en.png[align="center",title="Importing node labels containing double quotes"]
+{nbsp} +
+
+== Importing more than labels: nodes and edges attributes
+//ST: Importing more than labels: nodes and edges attributes
+
+//ST: !
+To import attributes we will need to proceed differently.
+
+We need 2 text files: one for the list of nodes, one for the list of relations (edges)
+
+//ST: !
+
+An example file with a list of nodes:
+....
+Id,Label,Date of Birth,Place of Birth,Years of experience,Rating <1>
+3,"Dubois, Jeremy ""Danger""",17/09/1980,"Paris",8,9.27
+1,"Jeremy,Jennifer",25/03/1978,"Tampa",8,4.34
+45,"Rodriguez, Valerian",30/04/1985,"Berlin",5,6.66
+....
+<1> Nodes *must* have at least an Id and a Label. Don't put spaces after the commas
+
+//ST: !
+An example file with a list of edges:
+....
+Source,Target,Weight,Type,Where first met <1>
+1,45,3,"undirected",London <2>
+....
+<1> Edges *must* have at least a Source and Target. Other fields are optional.
+<2> "undirected", the alternative is "directed". Directed edges have arrow heads.
+
+//ST: !
+Let's import the list of nodes first. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-nodes-with-attributes.png[align="center",title="Importing a list of nodes with attributes"]
+{nbsp} +
+
+//ST: !
+in the next screen, we must be careful with a couple of things:
+
+image::The-attributes-of-the-nodes.png[align="center",title="The attributes of the nodes"]
+{nbsp} +
+
+//ST: !
+Then we can import the file with the list of relations. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-edges-with-attributes.png[align="center",title="Importing a list of edges with attributes"]
+{nbsp} +
+
+//ST: !
+
+image::The-attributes-of-the-edges.png[align="center",title="The attributes of the edges"]
+{nbsp} +
+
+== Memo card
+//ST: Memo card
+
+//ST: !
+Nodes
+
+- header must be at least `Id,Label`
+
+//ST: !
+Edges
+
+- header must be at least `Source,Target`
+- To have an attribute for the "thickness" or "strength" of a relation, this attribute must be called "Weight".
+- Want arrows on your links? Add an attribute "Type", with value "Directed"
+- Don't wan't arrows? Add an attribute "Type", with value "Undirected"
+
+//ST: !
+Types of attributes: which to choose?
+
+image::en/import-spreadsheet-9-en.png[align="center",title="Kinds of attributes"]
+{nbsp} +
+
+//ST: !
+
+- Textual attribute: `String`. Nodes sharing the same textual value can be colored the same, or filtered together...
+- Numerical attribute: `Integer`, `Double` or `Float`. Nodes can be resized according to their value, or colorized in a gradient. Filters can be applied based on the range of values.
+- Boolean attribute: just a `true` or `false` value. Useful for filtering out some nodes which are true or false on some parameter.
+- Other types of attributes: not needed for the moment.
+
+
+== (to be continued)
+//ST: (to be continued)
+
+
+== More tutorials on importing data to Gephi
+//ST: More tutorials on importing data to Gephi
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-CSV-Data[The Gephi wiki on importing csv]
+- https://www.youtube.com/watch?v=3Im7vNRA2ns[Video "How to import a CSV into Gephi" by Jen Golbeck]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_pdf.md b/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_pdf.md
new file mode 100644
index 0000000..9804103
--- /dev/null
+++ b/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_pdf.md
@@ -0,0 +1,243 @@
+= Importing data in Gephi
+Clément Levallois
+2017-02-03
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Importing an existing network file
+//ST: Importing an existing network file
+
+//ST: !
+Gephi handles the import of network files in a variety of formats:
+
+image::network-formats-gephi-import-en.png[align="center",title="file formats supported by Gephi. Source: gephi.org"]
+{nbsp} +
+
+//ST: !
+To import any of these files, just choose the `File -> Open` menu
+
+== Importing nodes with labels and their relations: simplest version
+
+//ST: Importing nodes with labels and their relations: simplest version
+
+(looking for the complete, full featured csv import version? <>.)
+
+//ST: !
+The easiest is probably to just have a text file like this one:
+
+----
+Source,Target <1>
+Jeremy,Jennifer
+Valerian,Jeremy
+----
+
+<1> This line is mandatory. Don't put a space between the comma and "Target"
+
+Just write this in a text editor and save it as a file `example.txt`
+
+//ST: !
+
+To import this file in Gephi, go to `File->Import Spreadsheet`. Then in the window:
+
+image::Importing-nodes-with-labels-and-their-relations.png[align="center",title="Importing nodes with labels and their relations"]
+{nbsp} +
+
+//ST: !
+In this window, make sure the box "Create missing nodes" is checked:
+
+image::Second-screen.png[align="center",title="Second screen"]
+{nbsp} +
+
+[[full-csv-version]]
+//ST: Importing nodes with labels and their relations: full version
+
+//ST: !
+Let's look again at the simple text file we used:
+
+....
+Source,Target
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+
+There are 2 issues and several missing features, listed below:
+
+//ST: !
+==== 1. labels containing commas in them will not work.
+
+Imagine our characters have first names and last names separated by a comma:
+
+Dubois, Jeremy
+
+Rodriguez, Valerian
+
+//ST: !
+This will look like:
+
+....
+Source,Target
+Dubois, Jeremy,Jeremy,Jennifer
+Rodriguez, Valerian,Dubois, Jeremy
+....
+
+//ST: !
+
+Note that we have 3 commas per line, instead of one!
+In this mess, Gephi will not detect where the node labels start and end.
+The import will break.
+
+//ST: !
+The proper solution is to put node labels inside *double quotes*:
+
+....
+Source,Target
+"Dubois, Jeremy","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy"
+....
+
+Be careful to use these straight " " double quotes, not the curvy ones: “ ” which French keyboards sometimes have.
+
+//ST: !
+==== 2. labels containing double quotes " " in them will not work.
+
+Example: let's imagine that one of our characters has a middle name: Jeremy "Danger" Dubois.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy "Danger"","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy "Danger""
+....
+
+//ST: !
+These extra " " will make the Gephi import break:
+
+image::en/import-spreadsheet-4-en.png[align="center",title="The import bugs - the middle name has disappeared"]
+{nbsp} +
+
+
+//ST: !
+The solution consists in *adding an extra double quote in front of the double quotes*.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy ""Danger""","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy ""Danger"""
+....
+
+//ST: !
+This time, Gephi imports the network correctly:
+
+image::en/import-spreadsheet-3-en.png[align="center",title="Importing node labels containing double quotes"]
+{nbsp} +
+
+== Importing more than labels: nodes and edges attributes
+//ST: Importing more than labels: nodes and edges attributes
+
+//ST: !
+To import attributes we will need to proceed differently.
+
+We need 2 text files: one for the list of nodes, one for the list of relations (edges)
+
+//ST: !
+
+An example file with a list of nodes:
+....
+Id,Label,Date of Birth,Place of Birth,Years of experience,Rating <1>
+3,"Dubois, Jeremy ""Danger""",17/09/1980,"Paris",8,9.27
+1,"Jeremy,Jennifer",25/03/1978,"Tampa",8,4.34
+45,"Rodriguez, Valerian",30/04/1985,"Berlin",5,6.66
+....
+<1> Nodes *must* have at least an Id and a Label. Don't put spaces after the commas
+
+//ST: !
+An example file with a list of edges:
+....
+Source,Target,Weight,Type,Where first met <1>
+1,45,3,"undirected",London <2>
+....
+<1> Edges *must* have at least a Source and Target. Other fields are optional.
+<2> "undirected", the alternative is "directed". Directed edges have arrow heads.
+
+//ST: !
+Let's import the list of nodes first. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-nodes-with-attributes.png[align="center",title="Importing a list of nodes with attributes"]
+{nbsp} +
+
+//ST: !
+in the next screen, we must be careful with a couple of things:
+
+image::The-attributes-of-the-nodes.png[align="center",title="The attributes of the nodes"]
+{nbsp} +
+
+//ST: !
+Then we can import the file with the list of relations. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-edges-with-attributes.png[align="center",title="Importing a list of edges with attributes"]
+{nbsp} +
+
+//ST: !
+
+image::The-attributes-of-the-edges.png[align="center",title="The attributes of the edges"]
+{nbsp} +
+
+== Memo card
+//ST: Memo card
+
+//ST: !
+Nodes
+
+- header must be at least `Id,Label`
+
+//ST: !
+Edges
+
+- header must be at least `Source,Target`
+- To have an attribute for the "thickness" or "strength" of a relation, this attribute must be called "Weight".
+- Want arrows on your links? Add an attribute "Type", with value "Directed"
+- Don't wan't arrows? Add an attribute "Type", with value "Undirected"
+
+//ST: !
+Types of attributes: which to choose?
+
+image::en/import-spreadsheet-9-en.png[align="center",title="Kinds of attributes"]
+{nbsp} +
+
+//ST: !
+
+- Textual attribute: `String`. Nodes sharing the same textual value can be colored the same, or filtered together...
+- Numerical attribute: `Integer`, `Double` or `Float`. Nodes can be resized according to their value, or colorized in a gradient. Filters can be applied based on the range of values.
+- Boolean attribute: just a `true` or `false` value. Useful for filtering out some nodes which are true or false on some parameter.
+- Other types of attributes: not needed for the moment.
+
+
+== (to be continued)
+//ST: (to be continued)
+
+
+== More tutorials on importing data to Gephi
+//ST: More tutorials on importing data to Gephi
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-CSV-Data[The Gephi wiki on importing csv]
+- https://www.youtube.com/watch?v=3Im7vNRA2ns[Video "How to import a CSV into Gephi" by Jen Golbeck]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_slides.md b/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_slides.md
new file mode 100644
index 0000000..4401bfe
--- /dev/null
+++ b/docs/generated-pdf/subdir/importing-csv-data-in-gephi-en_temp_slides.md
@@ -0,0 +1,263 @@
+= Importing data in Gephi
+Clément Levallois
+2017-02-03
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Importing an existing network file
+
+== !
+Gephi handles the import of network files in a variety of formats:
+
+[.stretch]
+image::network-formats-gephi-import-en.png[align="center",title="file formats supported by Gephi. Source: gephi.org"]
+
+
+== !
+To import any of these files, just choose the `File -> Open` menu
+
+
+== Importing nodes with labels and their relations: simplest version
+
+(looking for the complete, full featured csv import version? <>.)
+
+== !
+The easiest is probably to just have a text file like this one:
+
+----
+Source,Target <1>
+Jeremy,Jennifer
+Valerian,Jeremy
+----
+
+<1> This line is mandatory. Don't put a space between the comma and "Target"
+
+Just write this in a text editor and save it as a file `example.txt`
+
+== !
+
+To import this file in Gephi, go to `File->Import Spreadsheet`. Then in the window:
+
+[.stretch]
+image::Importing-nodes-with-labels-and-their-relations.png[align="center",title="Importing nodes with labels and their relations"]
+
+
+== !
+In this window, make sure the box "Create missing nodes" is checked:
+
+[.stretch]
+image::Second-screen.png[align="center",title="Second screen"]
+
+
+[[full-csv-version]]
+== Importing nodes with labels and their relations: full version
+
+== !
+Let's look again at the simple text file we used:
+
+....
+Source,Target
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+
+There are 2 issues and several missing features, listed below:
+
+== !
+==== 1. labels containing commas in them will not work.
+
+Imagine our characters have first names and last names separated by a comma:
+
+Dubois, Jeremy
+
+Rodriguez, Valerian
+
+== !
+This will look like:
+
+....
+Source,Target
+Dubois, Jeremy,Jeremy,Jennifer
+Rodriguez, Valerian,Dubois, Jeremy
+....
+
+== !
+
+Note that we have 3 commas per line, instead of one!
+In this mess, Gephi will not detect where the node labels start and end.
+The import will break.
+
+== !
+The proper solution is to put node labels inside *double quotes*:
+
+....
+Source,Target
+"Dubois, Jeremy","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy"
+....
+
+Be careful to use these straight " " double quotes, not the curvy ones: “ ” which French keyboards sometimes have.
+
+== !
+==== 2. labels containing double quotes " " in them will not work.
+
+Example: let's imagine that one of our characters has a middle name: Jeremy "Danger" Dubois.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy "Danger"","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy "Danger""
+....
+
+== !
+These extra " " will make the Gephi import break:
+
+[.stretch]
+image::en/import-spreadsheet-4-en.png[align="center",title="The import bugs - the middle name has disappeared"]
+
+
+
+== !
+The solution consists in *adding an extra double quote in front of the double quotes*.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy ""Danger""","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy ""Danger"""
+....
+
+== !
+This time, Gephi imports the network correctly:
+
+[.stretch]
+image::en/import-spreadsheet-3-en.png[align="center",title="Importing node labels containing double quotes"]
+
+
+== Importing more than labels: nodes and edges attributes
+
+== !
+To import attributes we will need to proceed differently.
+
+We need 2 text files: one for the list of nodes, one for the list of relations (edges)
+
+== !
+
+An example file with a list of nodes:
+....
+Id,Label,Date of Birth,Place of Birth,Years of experience,Rating <1>
+3,"Dubois, Jeremy ""Danger""",17/09/1980,"Paris",8,9.27
+1,"Jeremy,Jennifer",25/03/1978,"Tampa",8,4.34
+45,"Rodriguez, Valerian",30/04/1985,"Berlin",5,6.66
+....
+<1> Nodes *must* have at least an Id and a Label. Don't put spaces after the commas
+
+== !
+An example file with a list of edges:
+....
+Source,Target,Weight,Type,Where first met <1>
+1,45,3,"undirected",London <2>
+....
+<1> Edges *must* have at least a Source and Target. Other fields are optional.
+<2> "undirected", the alternative is "directed". Directed edges have arrow heads.
+
+== !
+Let's import the list of nodes first. `File -> Import Spreadsheet`
+
+[.stretch]
+image::Importing-a-list-of-nodes-with-attributes.png[align="center",title="Importing a list of nodes with attributes"]
+
+
+== !
+in the next screen, we must be careful with a couple of things:
+
+[.stretch]
+image::The-attributes-of-the-nodes.png[align="center",title="The attributes of the nodes"]
+
+
+== !
+Then we can import the file with the list of relations. `File -> Import Spreadsheet`
+
+[.stretch]
+image::Importing-a-list-of-edges-with-attributes.png[align="center",title="Importing a list of edges with attributes"]
+
+
+== !
+
+[.stretch]
+image::The-attributes-of-the-edges.png[align="center",title="The attributes of the edges"]
+
+
+== Memo card
+
+== !
+Nodes
+
+- header must be at least `Id,Label`
+
+== !
+Edges
+
+- header must be at least `Source,Target`
+- To have an attribute for the "thickness" or "strength" of a relation, this attribute must be called "Weight".
+- Want arrows on your links? Add an attribute "Type", with value "Directed"
+- Don't wan't arrows? Add an attribute "Type", with value "Undirected"
+
+== !
+Types of attributes: which to choose?
+
+[.stretch]
+image::en/import-spreadsheet-9-en.png[align="center",title="Kinds of attributes"]
+
+
+== !
+
+- Textual attribute: `String`. Nodes sharing the same textual value can be colored the same, or filtered together...
+- Numerical attribute: `Integer`, `Double` or `Float`. Nodes can be resized according to their value, or colorized in a gradient. Filters can be applied based on the range of values.
+- Boolean attribute: just a `true` or `false` value. Useful for filtering out some nodes which are true or false on some parameter.
+- Other types of attributes: not needed for the moment.
+
+
+== (to be continued)
+
+
+== More tutorials on importing data to Gephi
+== !
+
+- https://github.com/gephi/gephi/wiki/Import-CSV-Data[The Gephi wiki on importing csv]
+- https://www.youtube.com/watch?v=3Im7vNRA2ns[Video "How to import a CSV into Gephi" by Jen Golbeck]
+
+
+== The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_common.md b/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_common.md
new file mode 100644
index 0000000..73cbad9
--- /dev/null
+++ b/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_common.md
@@ -0,0 +1,230 @@
+= Importing data in Gephi
+Clément Levallois
+2017-02-03
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+//ST: 'Escape' to see all sides, F11 for full screen
+
+== Importing an existing network file
+//ST: Importing an existing network file
+
+//ST: !
+Gephi handles the import of network files in a variety of formats:
+
+image::network-formats-gephi-import-en.png[align="center",title="file formats supported by Gephi. Source: gephi.org"]
+
+//ST: !
+To import any of these files, just choose the `File -> Open` menu
+
+== Importing nodes with labels and their relations: simplest version
+
+//ST: Importing nodes with labels and their relations: simplest version
+
+(looking for the complete, full featured csv import version? <>.)
+
+//ST: !
+The easiest is probably to just have a text file like this one:
+
+....
+Source,Target <1>
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+<1>This line is mandatory. Don't put a space between the comma and "Target"
+
+Just write this in a text editor and save it as a file `example.txt`
+
+//ST: !
+
+To import this file in Gephi, go to `File->Import Spreadsheet`. Then in the window:
+
+image::Importing-nodes-with-labels-and-their-relations.png[align="center",title="Importing nodes with labels and their relations"]
+
+//ST: !
+In this window, make sure the box "Create missing nodes" is checked:
+
+image::Second-screen.png[align="center",title="Second screen"]
+
+[[full-csv-version]]
+//ST: Importing nodes with labels and their relations: full version
+
+//ST: !
+Let's look again at the simple text file we used:
+
+....
+Source,Target
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+
+There are 2 issues and several missing features, listed below:
+
+//ST: !
+==== 1. labels containing commas in them will not work.
+
+Imagine our characters have first names and last names separated by a comma:
+
+Dubois, Jeremy
+
+Rodriguez, Valerian
+
+//ST: !
+This will look like:
+
+....
+Source,Target
+Dubois, Jeremy,Jeremy,Jennifer
+Rodriguez, Valerian,Dubois, Jeremy
+....
+
+//ST: !
+
+Note that we have 3 commas per line, instead of one!
+In this mess, Gephi will not detect where the node labels start and end.
+The import will break.
+
+//ST: !
+The proper solution is to put node labels inside *double quotes*:
+
+....
+Source,Target
+"Dubois, Jeremy","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy"
+....
+
+Be careful to use these straight " " double quotes, not the curvy ones: “ ” which French keyboards sometimes have.
+
+//ST: !
+==== 2. labels containing double quotes " " in them will not work.
+
+Example: let's imagine that one of our characters has a middle name: Jeremy "Danger" Dubois.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy "Danger"","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy "Danger""
+....
+
+//ST: !
+These extra " " will make the Gephi import break:
+
+image::en/import-spreadsheet-4-en.png[align="center",title="The import bugs - the middle name has disappeared"]
+
+
+//ST: !
+The solution consists in *adding an extra double quote in front of the double quotes*.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy ""Danger""","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy ""Danger"""
+....
+
+//ST: !
+This time, Gephi imports the network correctly:
+
+image::en/import-spreadsheet-3-en.png[align="center",title="Importing node labels containing double quotes"]
+
+== Importing more than labels: nodes and edges attributes
+//ST: Importing more than labels: nodes and edges attributes
+
+//ST: !
+To import attributes we will need to proceed differently.
+
+We need 2 text files: one for the list of nodes, one for the list of relations (edges)
+
+//ST: !
+
+An example file with a list of nodes:
+....
+Id,Label,Date of Birth,Place of Birth,Years of experience,Rating <1>
+3,"Dubois, Jeremy ""Danger""",17/09/1980,"Paris",8,9.27
+1,"Jeremy,Jennifer",25/03/1978,"Tampa",8,4.34
+45,"Rodriguez, Valerian",30/04/1985,"Berlin",5,6.66
+....
+<1> Nodes *must* have at least an Id and a Label. Don't put spaces after the commas
+
+//ST: !
+An example file with a list of edges:
+....
+Source,Target,Weight,Type,Where first met <1>
+1,45,3,"undirected",London <2>
+....
+<1> Edges *must* have at least a Source and Target. Other fields are optional.
+<2> "undirected", the alternative is "directed". Directed edges have arrow heads.
+
+//ST: !
+Let's import the list of nodes first. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-nodes-with-attributes.png[align="center",title="Importing a list of nodes with attributes"]
+
+//ST: !
+in the next screen, we must be careful with a couple of things:
+
+image::The-attributes-of-the-nodes.png[align="center",title="The attributes of the nodes"]
+
+//ST: !
+Then we can import the file with the list of relations. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-edges-with-attributes.png[align="center",title="Importing a list of edges with attributes"]
+
+//ST: !
+
+image::The-attributes-of-the-edges.png[align="center",title="The attributes of the edges"]
+
+== Memo card
+//ST: Memo card
+
+//ST: !
+Nodes
+
+- header must be at least `Id,Label`
+
+//ST: !
+Edges
+
+- header must be at least `Source,Target`
+- To have an attribute for the "thickness" or "strength" of a relation, this attribute must be called "Weight".
+- Want arrows on your links? Add an attribute "Type", with value "Directed"
+- Don't wan't arrows? Add an attribute "Type", with value "Undirected"
+
+//ST: !
+Types of attributes: which to choose?
+
+image::en/import-spreadsheet-9-en.png[align="center",title="Kinds of attributes"]
+
+//ST: !
+
+- Textual attribute: `String`. Nodes sharing the same textual value can be colored the same, or filtered together...
+- Numerical attribute: `Integer`, `Double` or `Float`. Nodes can be resized according to their value, or colorized in a gradient. Filters can be applied based on the range of values.
+- Boolean attribute: just a `true` or `false` value. Useful for filtering out some nodes which are true or false on some parameter.
+- Other types of attributes: not needed for the moment.
+
+
+== (to be continued)
+//ST: (to be continued)
+
+
+== More tutorials on importing data to Gephi
+//ST: More tutorials on importing data to Gephi
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-CSV-Data[The Gephi wiki on importing csv]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_html.md b/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_html.md
new file mode 100644
index 0000000..73cbad9
--- /dev/null
+++ b/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_html.md
@@ -0,0 +1,230 @@
+= Importing data in Gephi
+Clément Levallois
+2017-02-03
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+//ST: 'Escape' to see all sides, F11 for full screen
+
+== Importing an existing network file
+//ST: Importing an existing network file
+
+//ST: !
+Gephi handles the import of network files in a variety of formats:
+
+image::network-formats-gephi-import-en.png[align="center",title="file formats supported by Gephi. Source: gephi.org"]
+
+//ST: !
+To import any of these files, just choose the `File -> Open` menu
+
+== Importing nodes with labels and their relations: simplest version
+
+//ST: Importing nodes with labels and their relations: simplest version
+
+(looking for the complete, full featured csv import version? <>.)
+
+//ST: !
+The easiest is probably to just have a text file like this one:
+
+....
+Source,Target <1>
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+<1>This line is mandatory. Don't put a space between the comma and "Target"
+
+Just write this in a text editor and save it as a file `example.txt`
+
+//ST: !
+
+To import this file in Gephi, go to `File->Import Spreadsheet`. Then in the window:
+
+image::Importing-nodes-with-labels-and-their-relations.png[align="center",title="Importing nodes with labels and their relations"]
+
+//ST: !
+In this window, make sure the box "Create missing nodes" is checked:
+
+image::Second-screen.png[align="center",title="Second screen"]
+
+[[full-csv-version]]
+//ST: Importing nodes with labels and their relations: full version
+
+//ST: !
+Let's look again at the simple text file we used:
+
+....
+Source,Target
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+
+There are 2 issues and several missing features, listed below:
+
+//ST: !
+==== 1. labels containing commas in them will not work.
+
+Imagine our characters have first names and last names separated by a comma:
+
+Dubois, Jeremy
+
+Rodriguez, Valerian
+
+//ST: !
+This will look like:
+
+....
+Source,Target
+Dubois, Jeremy,Jeremy,Jennifer
+Rodriguez, Valerian,Dubois, Jeremy
+....
+
+//ST: !
+
+Note that we have 3 commas per line, instead of one!
+In this mess, Gephi will not detect where the node labels start and end.
+The import will break.
+
+//ST: !
+The proper solution is to put node labels inside *double quotes*:
+
+....
+Source,Target
+"Dubois, Jeremy","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy"
+....
+
+Be careful to use these straight " " double quotes, not the curvy ones: “ ” which French keyboards sometimes have.
+
+//ST: !
+==== 2. labels containing double quotes " " in them will not work.
+
+Example: let's imagine that one of our characters has a middle name: Jeremy "Danger" Dubois.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy "Danger"","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy "Danger""
+....
+
+//ST: !
+These extra " " will make the Gephi import break:
+
+image::en/import-spreadsheet-4-en.png[align="center",title="The import bugs - the middle name has disappeared"]
+
+
+//ST: !
+The solution consists in *adding an extra double quote in front of the double quotes*.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy ""Danger""","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy ""Danger"""
+....
+
+//ST: !
+This time, Gephi imports the network correctly:
+
+image::en/import-spreadsheet-3-en.png[align="center",title="Importing node labels containing double quotes"]
+
+== Importing more than labels: nodes and edges attributes
+//ST: Importing more than labels: nodes and edges attributes
+
+//ST: !
+To import attributes we will need to proceed differently.
+
+We need 2 text files: one for the list of nodes, one for the list of relations (edges)
+
+//ST: !
+
+An example file with a list of nodes:
+....
+Id,Label,Date of Birth,Place of Birth,Years of experience,Rating <1>
+3,"Dubois, Jeremy ""Danger""",17/09/1980,"Paris",8,9.27
+1,"Jeremy,Jennifer",25/03/1978,"Tampa",8,4.34
+45,"Rodriguez, Valerian",30/04/1985,"Berlin",5,6.66
+....
+<1> Nodes *must* have at least an Id and a Label. Don't put spaces after the commas
+
+//ST: !
+An example file with a list of edges:
+....
+Source,Target,Weight,Type,Where first met <1>
+1,45,3,"undirected",London <2>
+....
+<1> Edges *must* have at least a Source and Target. Other fields are optional.
+<2> "undirected", the alternative is "directed". Directed edges have arrow heads.
+
+//ST: !
+Let's import the list of nodes first. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-nodes-with-attributes.png[align="center",title="Importing a list of nodes with attributes"]
+
+//ST: !
+in the next screen, we must be careful with a couple of things:
+
+image::The-attributes-of-the-nodes.png[align="center",title="The attributes of the nodes"]
+
+//ST: !
+Then we can import the file with the list of relations. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-edges-with-attributes.png[align="center",title="Importing a list of edges with attributes"]
+
+//ST: !
+
+image::The-attributes-of-the-edges.png[align="center",title="The attributes of the edges"]
+
+== Memo card
+//ST: Memo card
+
+//ST: !
+Nodes
+
+- header must be at least `Id,Label`
+
+//ST: !
+Edges
+
+- header must be at least `Source,Target`
+- To have an attribute for the "thickness" or "strength" of a relation, this attribute must be called "Weight".
+- Want arrows on your links? Add an attribute "Type", with value "Directed"
+- Don't wan't arrows? Add an attribute "Type", with value "Undirected"
+
+//ST: !
+Types of attributes: which to choose?
+
+image::en/import-spreadsheet-9-en.png[align="center",title="Kinds of attributes"]
+
+//ST: !
+
+- Textual attribute: `String`. Nodes sharing the same textual value can be colored the same, or filtered together...
+- Numerical attribute: `Integer`, `Double` or `Float`. Nodes can be resized according to their value, or colorized in a gradient. Filters can be applied based on the range of values.
+- Boolean attribute: just a `true` or `false` value. Useful for filtering out some nodes which are true or false on some parameter.
+- Other types of attributes: not needed for the moment.
+
+
+== (to be continued)
+//ST: (to be continued)
+
+
+== More tutorials on importing data to Gephi
+//ST: More tutorials on importing data to Gephi
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-CSV-Data[The Gephi wiki on importing csv]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_pdf.md b/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_pdf.md
new file mode 100644
index 0000000..73cbad9
--- /dev/null
+++ b/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_pdf.md
@@ -0,0 +1,230 @@
+= Importing data in Gephi
+Clément Levallois
+2017-02-03
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+//ST: 'Escape' to see all sides, F11 for full screen
+
+== Importing an existing network file
+//ST: Importing an existing network file
+
+//ST: !
+Gephi handles the import of network files in a variety of formats:
+
+image::network-formats-gephi-import-en.png[align="center",title="file formats supported by Gephi. Source: gephi.org"]
+
+//ST: !
+To import any of these files, just choose the `File -> Open` menu
+
+== Importing nodes with labels and their relations: simplest version
+
+//ST: Importing nodes with labels and their relations: simplest version
+
+(looking for the complete, full featured csv import version? <>.)
+
+//ST: !
+The easiest is probably to just have a text file like this one:
+
+....
+Source,Target <1>
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+<1>This line is mandatory. Don't put a space between the comma and "Target"
+
+Just write this in a text editor and save it as a file `example.txt`
+
+//ST: !
+
+To import this file in Gephi, go to `File->Import Spreadsheet`. Then in the window:
+
+image::Importing-nodes-with-labels-and-their-relations.png[align="center",title="Importing nodes with labels and their relations"]
+
+//ST: !
+In this window, make sure the box "Create missing nodes" is checked:
+
+image::Second-screen.png[align="center",title="Second screen"]
+
+[[full-csv-version]]
+//ST: Importing nodes with labels and their relations: full version
+
+//ST: !
+Let's look again at the simple text file we used:
+
+....
+Source,Target
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+
+There are 2 issues and several missing features, listed below:
+
+//ST: !
+==== 1. labels containing commas in them will not work.
+
+Imagine our characters have first names and last names separated by a comma:
+
+Dubois, Jeremy
+
+Rodriguez, Valerian
+
+//ST: !
+This will look like:
+
+....
+Source,Target
+Dubois, Jeremy,Jeremy,Jennifer
+Rodriguez, Valerian,Dubois, Jeremy
+....
+
+//ST: !
+
+Note that we have 3 commas per line, instead of one!
+In this mess, Gephi will not detect where the node labels start and end.
+The import will break.
+
+//ST: !
+The proper solution is to put node labels inside *double quotes*:
+
+....
+Source,Target
+"Dubois, Jeremy","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy"
+....
+
+Be careful to use these straight " " double quotes, not the curvy ones: “ ” which French keyboards sometimes have.
+
+//ST: !
+==== 2. labels containing double quotes " " in them will not work.
+
+Example: let's imagine that one of our characters has a middle name: Jeremy "Danger" Dubois.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy "Danger"","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy "Danger""
+....
+
+//ST: !
+These extra " " will make the Gephi import break:
+
+image::en/import-spreadsheet-4-en.png[align="center",title="The import bugs - the middle name has disappeared"]
+
+
+//ST: !
+The solution consists in *adding an extra double quote in front of the double quotes*.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy ""Danger""","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy ""Danger"""
+....
+
+//ST: !
+This time, Gephi imports the network correctly:
+
+image::en/import-spreadsheet-3-en.png[align="center",title="Importing node labels containing double quotes"]
+
+== Importing more than labels: nodes and edges attributes
+//ST: Importing more than labels: nodes and edges attributes
+
+//ST: !
+To import attributes we will need to proceed differently.
+
+We need 2 text files: one for the list of nodes, one for the list of relations (edges)
+
+//ST: !
+
+An example file with a list of nodes:
+....
+Id,Label,Date of Birth,Place of Birth,Years of experience,Rating <1>
+3,"Dubois, Jeremy ""Danger""",17/09/1980,"Paris",8,9.27
+1,"Jeremy,Jennifer",25/03/1978,"Tampa",8,4.34
+45,"Rodriguez, Valerian",30/04/1985,"Berlin",5,6.66
+....
+<1> Nodes *must* have at least an Id and a Label. Don't put spaces after the commas
+
+//ST: !
+An example file with a list of edges:
+....
+Source,Target,Weight,Type,Where first met <1>
+1,45,3,"undirected",London <2>
+....
+<1> Edges *must* have at least a Source and Target. Other fields are optional.
+<2> "undirected", the alternative is "directed". Directed edges have arrow heads.
+
+//ST: !
+Let's import the list of nodes first. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-nodes-with-attributes.png[align="center",title="Importing a list of nodes with attributes"]
+
+//ST: !
+in the next screen, we must be careful with a couple of things:
+
+image::The-attributes-of-the-nodes.png[align="center",title="The attributes of the nodes"]
+
+//ST: !
+Then we can import the file with the list of relations. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-edges-with-attributes.png[align="center",title="Importing a list of edges with attributes"]
+
+//ST: !
+
+image::The-attributes-of-the-edges.png[align="center",title="The attributes of the edges"]
+
+== Memo card
+//ST: Memo card
+
+//ST: !
+Nodes
+
+- header must be at least `Id,Label`
+
+//ST: !
+Edges
+
+- header must be at least `Source,Target`
+- To have an attribute for the "thickness" or "strength" of a relation, this attribute must be called "Weight".
+- Want arrows on your links? Add an attribute "Type", with value "Directed"
+- Don't wan't arrows? Add an attribute "Type", with value "Undirected"
+
+//ST: !
+Types of attributes: which to choose?
+
+image::en/import-spreadsheet-9-en.png[align="center",title="Kinds of attributes"]
+
+//ST: !
+
+- Textual attribute: `String`. Nodes sharing the same textual value can be colored the same, or filtered together...
+- Numerical attribute: `Integer`, `Double` or `Float`. Nodes can be resized according to their value, or colorized in a gradient. Filters can be applied based on the range of values.
+- Boolean attribute: just a `true` or `false` value. Useful for filtering out some nodes which are true or false on some parameter.
+- Other types of attributes: not needed for the moment.
+
+
+== (to be continued)
+//ST: (to be continued)
+
+
+== More tutorials on importing data to Gephi
+//ST: More tutorials on importing data to Gephi
+//ST: !
+
+- https://github.com/gephi/gephi/wiki/Import-CSV-Data[The Gephi wiki on importing csv]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_slides.md b/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_slides.md
new file mode 100644
index 0000000..1b17ae5
--- /dev/null
+++ b/docs/generated-pdf/subdir/importing-data-in-gephi-en_temp_slides.md
@@ -0,0 +1,222 @@
+= Importing data in Gephi
+Clément Levallois
+2017-02-03
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+== 'Escape' to see all sides, F11 for full screen
+
+== Importing an existing network file
+
+== !
+Gephi handles the import of network files in a variety of formats:
+
+image::network-formats-gephi-import-en.png[align="center",title="file formats supported by Gephi. Source: gephi.org"]
+
+== !
+To import any of these files, just choose the `File -> Open` menu
+
+
+== Importing nodes with labels and their relations: simplest version
+
+(looking for the complete, full featured csv import version? <>.)
+
+== !
+The easiest is probably to just have a text file like this one:
+
+....
+Source,Target <1>
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+<1>This line is mandatory. Don't put a space between the comma and "Target"
+
+Just write this in a text editor and save it as a file `example.txt`
+
+== !
+
+To import this file in Gephi, go to `File->Import Spreadsheet`. Then in the window:
+
+image::Importing-nodes-with-labels-and-their-relations.png[align="center",title="Importing nodes with labels and their relations"]
+
+== !
+In this window, make sure the box "Create missing nodes" is checked:
+
+image::Second-screen.png[align="center",title="Second screen"]
+
+[[full-csv-version]]
+== Importing nodes with labels and their relations: full version
+
+== !
+Let's look again at the simple text file we used:
+
+....
+Source,Target
+Jeremy,Jennifer
+Valerian,Jeremy
+....
+
+There are 2 issues and several missing features, listed below:
+
+== !
+==== 1. labels containing commas in them will not work.
+
+Imagine our characters have first names and last names separated by a comma:
+
+Dubois, Jeremy
+
+Rodriguez, Valerian
+
+== !
+This will look like:
+
+....
+Source,Target
+Dubois, Jeremy,Jeremy,Jennifer
+Rodriguez, Valerian,Dubois, Jeremy
+....
+
+== !
+
+Note that we have 3 commas per line, instead of one!
+In this mess, Gephi will not detect where the node labels start and end.
+The import will break.
+
+== !
+The proper solution is to put node labels inside *double quotes*:
+
+....
+Source,Target
+"Dubois, Jeremy","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy"
+....
+
+Be careful to use these straight " " double quotes, not the curvy ones: “ ” which French keyboards sometimes have.
+
+== !
+==== 2. labels containing double quotes " " in them will not work.
+
+Example: let's imagine that one of our characters has a middle name: Jeremy "Danger" Dubois.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy "Danger"","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy "Danger""
+....
+
+== !
+These extra " " will make the Gephi import break:
+
+image::en/import-spreadsheet-4-en.png[align="center",title="The import bugs - the middle name has disappeared"]
+
+
+== !
+The solution consists in *adding an extra double quote in front of the double quotes*.
+Our text file will look like:
+....
+Source,Target
+"Dubois, Jeremy ""Danger""","Jeremy,Jennifer"
+"Rodriguez, Valerian","Dubois, Jeremy ""Danger"""
+....
+
+== !
+This time, Gephi imports the network correctly:
+
+image::en/import-spreadsheet-3-en.png[align="center",title="Importing node labels containing double quotes"]
+
+== Importing more than labels: nodes and edges attributes
+
+== !
+To import attributes we will need to proceed differently.
+
+We need 2 text files: one for the list of nodes, one for the list of relations (edges)
+
+== !
+
+An example file with a list of nodes:
+....
+Id,Label,Date of Birth,Place of Birth,Years of experience,Rating <1>
+3,"Dubois, Jeremy ""Danger""",17/09/1980,"Paris",8,9.27
+1,"Jeremy,Jennifer",25/03/1978,"Tampa",8,4.34
+45,"Rodriguez, Valerian",30/04/1985,"Berlin",5,6.66
+....
+<1> Nodes *must* have at least an Id and a Label. Don't put spaces after the commas
+
+== !
+An example file with a list of edges:
+....
+Source,Target,Weight,Type,Where first met <1>
+1,45,3,"undirected",London <2>
+....
+<1> Edges *must* have at least a Source and Target. Other fields are optional.
+<2> "undirected", the alternative is "directed". Directed edges have arrow heads.
+
+== !
+Let's import the list of nodes first. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-nodes-with-attributes.png[align="center",title="Importing a list of nodes with attributes"]
+
+== !
+in the next screen, we must be careful with a couple of things:
+
+image::The-attributes-of-the-nodes.png[align="center",title="The attributes of the nodes"]
+
+== !
+Then we can import the file with the list of relations. `File -> Import Spreadsheet`
+
+image::Importing-a-list-of-edges-with-attributes.png[align="center",title="Importing a list of edges with attributes"]
+
+== !
+
+image::The-attributes-of-the-edges.png[align="center",title="The attributes of the edges"]
+
+== Memo card
+
+== !
+Nodes
+
+- header must be at least `Id,Label`
+
+== !
+Edges
+
+- header must be at least `Source,Target`
+- To have an attribute for the "thickness" or "strength" of a relation, this attribute must be called "Weight".
+- Want arrows on your links? Add an attribute "Type", with value "Directed"
+- Don't wan't arrows? Add an attribute "Type", with value "Undirected"
+
+== !
+Types of attributes: which to choose?
+
+image::en/import-spreadsheet-9-en.png[align="center",title="Kinds of attributes"]
+
+== !
+
+- Textual attribute: `String`. Nodes sharing the same textual value can be colored the same, or filtered together...
+- Numerical attribute: `Integer`, `Double` or `Float`. Nodes can be resized according to their value, or colorized in a gradient. Filters can be applied based on the range of values.
+- Boolean attribute: just a `true` or `false` value. Useful for filtering out some nodes which are true or false on some parameter.
+- Other types of attributes: not needed for the moment.
+
+
+== (to be continued)
+
+
+== More tutorials on importing data to Gephi
+== !
+
+- https://github.com/gephi/gephi/wiki/Import-CSV-Data[The Gephi wiki on importing csv]
+
+
+== The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_common.md b/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_common.md
new file mode 100644
index 0000000..a1d3645
--- /dev/null
+++ b/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_common.md
@@ -0,0 +1,247 @@
+= K-partite and Bipartite Graph (Multimode Networks Transformations)
+Matthieu Totet
+2017-01-29
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+//ST: Author and documentation
+== Author and documentation
+
+//ST: !
+
+The Multimode Networks Transformations plugin allows you transform a k-partite graph into a mono-partite graph.
+
+//ST: !
+
+Official page of the plugin: https://gephi.org/plugins/#/plugin/semantic[https://gephi.org/plugins/#/plugin/semantic]
+
+Plugin Author: https://github.com/jaroslav-kuchar[Jaroslav Kuchar]
+
+Plugin sources: https://github.com/totetmatt/gephi-plugins/tree/multimode-network[https://github.com/totetmatt/gephi-plugins/tree/multimode-network]
+
+Licensed under: GNU GENERAL PUBLIC LICENSE
+
+
+== K-partite, Bipartite … What is it ?
+
+//ST: K-partite, Bipartite … What is it ?
+
+Most of the time, when you create a graph, all the nodes are representing the same « kind » of object.
+
+* In a Social Network: Persons
+* In a Route Network: Bus Stops, Airports, Stations etc…
+* In a Geographical Network: Countries
+
+//ST: !
+
+But sometimes, you will encounter graphs that will have 2, 3 or multiple « kinds » of nodes where the links are almost exclusively going from one type to the other.
+These
+graphs are called **Multipartite Graphs** :
+
+//ST: !
+
+* If you have **2** kinds of nodes, it’s a **Bi**partite Graph (or **2**-partite)
+** Example : Social Network : Persons < - > Companies
+
+//ST: !
+
+* If you have **3** kinds of nodes it’s a** 3**-partite Graph
+** Example : Movie Network : Actors < - > Movies < - > Movie Companies
+
+//ST: !
+
+* If you have **k** kinds of nodes it’s a **k**-partite Graph
+
+//ST: !
+
+If you want a proper scientific definition, you can check the https://en.wikipedia.org/wiki/Multipartite_graph[Wikipedia] page about the subject.
+
+== The Dataset
+
+//ST: The dataset
+
+We will use a well known data set : The Cac 40 Administrators relationship from http://www.citoyennumerique.fr/?tag=cac40[www.citoyennumerique.fr.]
+
+The files for Gephi are accessible here : link::../../resources/k-partite-bi-partite.zip[Gexf files]
+
+The tutorial will be based on the **SimpleGraph**, but you also have access to the **FullGraph** to play with.
+
+//ST: !
+
+The CAC 40 represents the 40 most valuable company on the french stock market. A **company** has several **administrators ** and nothing prevent an **administrator** to be in multiple **companies**. So the graph is build such as **companies** are sources of edges and targets are the **administrators**.
+
+//ST: !
+
+This is a typical bi-partite graph. If you look on the data, part of the node has a property type **Administrator** and the other part has a property type **Company**.
+
+image::en/k-partite/k-partite-simple-graph.png[align="center",title="Cac 40 bipartite graph"]
+{nbsp} +
+
+
+//ST: !
+
+We can get quick observations:
+
+* Filtering by InDegree, we can check that Michel Pebereau and Jean-Martin Folz are in **5 companies** at the same time.
+* Filtering by OutDegree, we can check that St-Gobain and Total have **8 administrators** that are in other companies.
+
+But that could be done without graph, an ordered list on excel would have done the job.
+
+== How to deal with multipartite graphs ?
+
+//ST:!
+
+Having a k-partite graph makes somehow the graph unfriendly to read. Usually, the metrics will be difficult to interpret, and
+generating a good visualisation from it won't be trivial.
+
+One method here is to **reduce** the **bi-partite** graph into a **mono-partite** graph.
+
+//ST: !
+
+
+> By reducing, we will « lose » information but we gain in **readability** and **synthesis**,
+> that’s part of the work in data visualisation to see at which level we
+> want to go and how to deal with this trade of information. This will
+> mostly be driven by the question you want to answer.
+
+//ST: !
+
+The principle of the reduction is to consider a type of **nodes** as **edges** and build a new graph based on this rule.
+In our example we have 2 ways to go:
+
+* Either we decide that the nodes of type « Company » are the edges. It will generate a "**graph** of **Persons **"
+* Either we decide that the nodes of type « Person » are the edges. It will generate a "**graph** of **Company **"
+
+//ST: !
+
+> Here both graphs generated makes « sense » (network of
+> entities) but it might appear that in some other case, one of the generated graphs won't really be 'interesting'.
+> That’s part of your work to judge this aspect.
+
+Now, how to perform the transformation?
+
+== Using a Plugin
+
+//ST: Using a Plugin
+
+//ST: !
+
+The easiest way is to use the link::https://marketplace.gephi.org/plugin/multimode-networks-transformations-2/[Multimode Network Transformation plugin].
+The plugin is available on the Gephi app market and in the plugin manager in Gephi.
+
+//ST: !
+
+image::en/k-partite/k-partite-install-plugin.png[align="center",title="Plugin Install"]
+{nbsp} +
+
+//ST: !
+
+When it’s installed, you should be able to see or display the ** Multimode Network Transformation** tab.
+
+//ST: !
+
+Then, load the graph and :
+
+* Click on **Load Attributes** and select **Type** on **Attribute Type.**
+* For left matrix select **Administrator – Company** and right matrix **Company – Adminstrator**
+* Enable **Remove Edges** and** Remove Nodes**, and Click** Run
+
+//ST: !
+
+> The plugin will modify directly the graph on the current workspace. If you want to keep the original graph, be sure
+> you have copied your workspace.
+
+//ST: !
+
+image::en/k-partite/k-partite-config.png[align="center",title="MultiPartite_Gephi"]
+{nbsp} +
+
+
+//ST: !
+
+The plugins will actually to transform each relation like:
+
+(Administrator)**<-**(Company)**->**(Administrator)
+
+to
+
+(Administrator)**<-[**{weight=Nb of Common Companies}**]->**(Administrator)
+
+Now you should have a graph with only administrators on the graph preview.
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-p2p.png[align="center",title="Tuto_Multi_P2P"]
+{nbsp} +
+
+
+//ST: !
+
+To generate the relation between companies, replay the same steps
+from the original graph (duplicate the workspace0 again) and in the **Multimode Network Transformation** use :
+
+* Left Matrix : Company – Administrator
+* Right Matrix : Administrator – Company
+
+//ST: !
+
+image::en/k-partite/k-partite-config-2.png[align="center",title="MultiPartite_Gephi_2"]
+{nbsp} +
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-c2c.png[align="center",title="Tuto_Multi_C2C"]
+{nbsp} +
+
+//ST: !
+
+With the 2 new graphs, and playing with the weighted degree of the nodes, we are able to see some particularities:
+
+* The maximum of **common administrator** between company is **3**: Bouygues and Alstom with Olivier Bouygues, Patrick Kron, Georges Chodron de Courcel
+* **Half of the companies** are sharing at least 2 administrators.
+* The maximum of **common company** between person is **3**: Gerard Lamarche and Paul Demaray Jr see each other in Total, GDF Suez and Lafarge SA.
+* From all administrators that are in at least 2 companies,** 1/3 of them are at least in the _same_ 2 companies**.
+
+//ST: !
+
+Theses statements could have been deduced from the original graph, but now, the information is more visible and accessible, especially if you want to share it to a large public.
+
+//ST: !
+
+> The next steps are now mostly to look at graphs, and summarize all
+> the particularities into some kind of output (poster, interactive graph,
+> newspaper etc…)
+
+== Limitations
+
+//ST: Limitations
+
+The plugin works quite well, but sometimes it has some limitation on large graphs. Or sometimes you may want to reduce your graph and have a custom metric computed.
+Then the only alternative to that is to use a script that will generate the graph for you.
+
+== Benefits
+
+//ST: Conclusion
+
+Multipartite graphs are complex to analyse, but synthesizing them to lower partite graphs gives you a better vision and insights about your data.
+
+== The end
+
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_html.md b/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_html.md
new file mode 100644
index 0000000..139b862
--- /dev/null
+++ b/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_html.md
@@ -0,0 +1,264 @@
+= K-partite and Bipartite Graph (Multimode Networks Transformations)
+Matthieu Totet
+2017-01-29
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+//ST: Author and documentation
+== Author and documentation
+
+//ST: !
+
+The Multimode Networks Transformations plugin allows you transform a k-partite graph into a mono-partite graph.
+
+//ST: !
+
+Official page of the plugin: https://gephi.org/plugins/#/plugin/semantic[https://gephi.org/plugins/#/plugin/semantic]
+
+Plugin Author: https://github.com/jaroslav-kuchar[Jaroslav Kuchar]
+
+Plugin sources: https://github.com/totetmatt/gephi-plugins/tree/multimode-network[https://github.com/totetmatt/gephi-plugins/tree/multimode-network]
+
+Licensed under: GNU GENERAL PUBLIC LICENSE
+
+
+== K-partite, Bipartite … What is it ?
+
+//ST: K-partite, Bipartite … What is it ?
+
+Most of the time, when you create a graph, all the nodes are representing the same « kind » of object.
+
+* In a Social Network: Persons
+* In a Route Network: Bus Stops, Airports, Stations etc…
+* In a Geographical Network: Countries
+
+//ST: !
+
+But sometimes, you will encounter graphs that will have 2, 3 or multiple « kinds » of nodes where the links are almost exclusively going from one type to the other.
+These
+graphs are called **Multipartite Graphs** :
+
+//ST: !
+
+* If you have **2** kinds of nodes, it’s a **Bi**partite Graph (or **2**-partite)
+** Example : Social Network : Persons < - > Companies
+
+//ST: !
+
+* If you have **3** kinds of nodes it’s a** 3**-partite Graph
+** Example : Movie Network : Actors < - > Movies < - > Movie Companies
+
+//ST: !
+
+* If you have **k** kinds of nodes it’s a **k**-partite Graph
+
+//ST: !
+
+If you want a proper scientific definition, you can check the https://en.wikipedia.org/wiki/Multipartite_graph[Wikipedia] page about the subject.
+
+== The Dataset
+
+//ST: The dataset
+
+We will use a well known data set : The Cac 40 Administrators relationship from http://www.citoyennumerique.fr/?tag=cac40[www.citoyennumerique.fr.]
+
+The files for Gephi are accessible here : link::../../resources/k-partite-bi-partite.zip[Gexf files]
+
+The tutorial will be based on the **SimpleGraph**, but you also have access to the **FullGraph** to play with.
+
+//ST: !
+
+The CAC 40 represents the 40 most valuable company on the french stock market. A **company** has several **administrators ** and nothing prevent an **administrator** to be in multiple **companies**. So the graph is build such as **companies** are sources of edges and targets are the **administrators**.
+
+//ST: !
+
+This is a typical bi-partite graph. If you look on the data, part of the node has a property type **Administrator** and the other part has a property type **Company**.
+
+image::en/k-partite/k-partite-simple-graph.png[align="center",title="Cac 40 bipartite graph"]
+{nbsp} +
+
+
+//ST: !
+
+We can get quick observations:
+
+* Filtering by InDegree, we can check that Michel Pebereau and Jean-Martin Folz are in **5 companies** at the same time.
+* Filtering by OutDegree, we can check that St-Gobain and Total have **8 administrators** that are in other companies.
+
+But that could be done without graph, an ordered list on excel would have done the job.
+
+== How to deal with multipartite graphs ?
+
+//ST:!
+
+Having a k-partite graph makes somehow the graph unfriendly to read. Usually, the metrics will be difficult to interpret, and
+generating a good visualisation from it won't be trivial.
+
+One method here is to **reduce** the **bi-partite** graph into a **mono-partite** graph.
+
+//ST: !
+
+
+> By reducing, we will « lose » information but we gain in **readability** and **synthesis**,
+> that’s part of the work in data visualisation to see at which level we
+> want to go and how to deal with this trade of information. This will
+> mostly be driven by the question you want to answer.
+
+//ST: !
+
+The principle of the reduction is to consider a type of **nodes** as **edges** and build a new graph based on this rule.
+In our example we have 2 ways to go:
+
+* Either we decide that the nodes of type « Company » are the edges. It will generate a "**graph** of **Persons **"
+* Either we decide that the nodes of type « Person » are the edges. It will generate a "**graph** of **Company **"
+
+//ST: !
+
+> Here both graphs generated makes « sense » (network of
+> entities) but it might appear that in some other case, one of the generated graphs won't really be 'interesting'.
+> That’s part of your work to judge this aspect.
+
+Now, how to perform the transformation?
+
+== Using a Plugin
+
+//ST: Using a Plugin
+
+//ST: !
+
+The easiest way is to use the link::https://marketplace.gephi.org/plugin/multimode-networks-transformations-2/[Multimode Network Transformation plugin].
+The plugin is available on the Gephi app market and in the plugin manager in Gephi.
+
+//ST: !
+
+image::en/k-partite/k-partite-install-plugin.png[align="center",title="Plugin Install"]
+{nbsp} +
+
+//ST: !
+
+When it’s installed, you should be able to see or display the ** Multimode Network Transformation** tab.
+
+//ST: !
+
+Then, load the graph and :
+
+* Click on **Load Attributes** and select **Type** on **Attribute Type.**
+* For left matrix select **Administrator – Company** and right matrix **Company – Adminstrator**
+* Enable **Remove Edges** and** Remove Nodes**, and Click** Run
+
+//ST: !
+
+> The plugin will modify directly the graph on the current workspace. If you want to keep the original graph, be sure
+> you have copied your workspace.
+
+//ST: !
+
+image::en/k-partite/k-partite-config.png[align="center",title="MultiPartite_Gephi"]
+{nbsp} +
+
+
+//ST: !
+
+The plugins will actually to transform each relation like:
+
+(Administrator)**<-**(Company)**->**(Administrator)
+
+to
+
+(Administrator)**<-[**{weight=Nb of Common Companies}**]->**(Administrator)
+
+Now you should have a graph with only administrators on the graph preview.
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-p2p.png[align="center",title="Tuto_Multi_P2P"]
+{nbsp} +
+
+
+//ST: !
+
+To generate the relation between companies, replay the same steps
+from the original graph (duplicate the workspace0 again) and in the **Multimode Network Transformation** use :
+
+* Left Matrix : Company – Administrator
+* Right Matrix : Administrator – Company
+
+//ST: !
+
+image::en/k-partite/k-partite-config-2.png[align="center",title="MultiPartite_Gephi_2"]
+{nbsp} +
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-c2c.png[align="center",title="Tuto_Multi_C2C"]
+{nbsp} +
+
+//ST: !
+
+With the 2 new graphs, and playing with the weighted degree of the nodes, we are able to see some particularities:
+
+* The maximum of **common administrator** between company is **3**: Bouygues and Alstom with Olivier Bouygues, Patrick Kron, Georges Chodron de Courcel
+* **Half of the companies** are sharing at least 2 administrators.
+* The maximum of **common company** between person is **3**: Gerard Lamarche and Paul Demaray Jr see each other in Total, GDF Suez and Lafarge SA.
+* From all administrators that are in at least 2 companies,** 1/3 of them are at least in the _same_ 2 companies**.
+
+//ST: !
+
+Theses statements could have been deduced from the original graph, but now, the information is more visible and accessible, especially if you want to share it to a large public.
+
+//ST: !
+
+> The next steps are now mostly to look at graphs, and summarize all
+> the particularities into some kind of output (poster, interactive graph,
+> newspaper etc…)
+
+== Limitations
+
+//ST: Limitations
+
+The plugin works quite well, but sometimes it has some limitation on large graphs. Or sometimes you may want to reduce your graph and have a custom metric computed.
+Then the only alternative to that is to use a script that will generate the graph for you.
+
+== Benefits
+
+//ST: Conclusion
+
+Multipartite graphs are complex to analyse, but synthesizing them to lower partite graphs gives you a better vision and insights about your data.
+
+== The end
+
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_pdf.md b/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_pdf.md
new file mode 100644
index 0000000..a1d3645
--- /dev/null
+++ b/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_pdf.md
@@ -0,0 +1,247 @@
+= K-partite and Bipartite Graph (Multimode Networks Transformations)
+Matthieu Totet
+2017-01-29
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+//ST: Author and documentation
+== Author and documentation
+
+//ST: !
+
+The Multimode Networks Transformations plugin allows you transform a k-partite graph into a mono-partite graph.
+
+//ST: !
+
+Official page of the plugin: https://gephi.org/plugins/#/plugin/semantic[https://gephi.org/plugins/#/plugin/semantic]
+
+Plugin Author: https://github.com/jaroslav-kuchar[Jaroslav Kuchar]
+
+Plugin sources: https://github.com/totetmatt/gephi-plugins/tree/multimode-network[https://github.com/totetmatt/gephi-plugins/tree/multimode-network]
+
+Licensed under: GNU GENERAL PUBLIC LICENSE
+
+
+== K-partite, Bipartite … What is it ?
+
+//ST: K-partite, Bipartite … What is it ?
+
+Most of the time, when you create a graph, all the nodes are representing the same « kind » of object.
+
+* In a Social Network: Persons
+* In a Route Network: Bus Stops, Airports, Stations etc…
+* In a Geographical Network: Countries
+
+//ST: !
+
+But sometimes, you will encounter graphs that will have 2, 3 or multiple « kinds » of nodes where the links are almost exclusively going from one type to the other.
+These
+graphs are called **Multipartite Graphs** :
+
+//ST: !
+
+* If you have **2** kinds of nodes, it’s a **Bi**partite Graph (or **2**-partite)
+** Example : Social Network : Persons < - > Companies
+
+//ST: !
+
+* If you have **3** kinds of nodes it’s a** 3**-partite Graph
+** Example : Movie Network : Actors < - > Movies < - > Movie Companies
+
+//ST: !
+
+* If you have **k** kinds of nodes it’s a **k**-partite Graph
+
+//ST: !
+
+If you want a proper scientific definition, you can check the https://en.wikipedia.org/wiki/Multipartite_graph[Wikipedia] page about the subject.
+
+== The Dataset
+
+//ST: The dataset
+
+We will use a well known data set : The Cac 40 Administrators relationship from http://www.citoyennumerique.fr/?tag=cac40[www.citoyennumerique.fr.]
+
+The files for Gephi are accessible here : link::../../resources/k-partite-bi-partite.zip[Gexf files]
+
+The tutorial will be based on the **SimpleGraph**, but you also have access to the **FullGraph** to play with.
+
+//ST: !
+
+The CAC 40 represents the 40 most valuable company on the french stock market. A **company** has several **administrators ** and nothing prevent an **administrator** to be in multiple **companies**. So the graph is build such as **companies** are sources of edges and targets are the **administrators**.
+
+//ST: !
+
+This is a typical bi-partite graph. If you look on the data, part of the node has a property type **Administrator** and the other part has a property type **Company**.
+
+image::en/k-partite/k-partite-simple-graph.png[align="center",title="Cac 40 bipartite graph"]
+{nbsp} +
+
+
+//ST: !
+
+We can get quick observations:
+
+* Filtering by InDegree, we can check that Michel Pebereau and Jean-Martin Folz are in **5 companies** at the same time.
+* Filtering by OutDegree, we can check that St-Gobain and Total have **8 administrators** that are in other companies.
+
+But that could be done without graph, an ordered list on excel would have done the job.
+
+== How to deal with multipartite graphs ?
+
+//ST:!
+
+Having a k-partite graph makes somehow the graph unfriendly to read. Usually, the metrics will be difficult to interpret, and
+generating a good visualisation from it won't be trivial.
+
+One method here is to **reduce** the **bi-partite** graph into a **mono-partite** graph.
+
+//ST: !
+
+
+> By reducing, we will « lose » information but we gain in **readability** and **synthesis**,
+> that’s part of the work in data visualisation to see at which level we
+> want to go and how to deal with this trade of information. This will
+> mostly be driven by the question you want to answer.
+
+//ST: !
+
+The principle of the reduction is to consider a type of **nodes** as **edges** and build a new graph based on this rule.
+In our example we have 2 ways to go:
+
+* Either we decide that the nodes of type « Company » are the edges. It will generate a "**graph** of **Persons **"
+* Either we decide that the nodes of type « Person » are the edges. It will generate a "**graph** of **Company **"
+
+//ST: !
+
+> Here both graphs generated makes « sense » (network of
+> entities) but it might appear that in some other case, one of the generated graphs won't really be 'interesting'.
+> That’s part of your work to judge this aspect.
+
+Now, how to perform the transformation?
+
+== Using a Plugin
+
+//ST: Using a Plugin
+
+//ST: !
+
+The easiest way is to use the link::https://marketplace.gephi.org/plugin/multimode-networks-transformations-2/[Multimode Network Transformation plugin].
+The plugin is available on the Gephi app market and in the plugin manager in Gephi.
+
+//ST: !
+
+image::en/k-partite/k-partite-install-plugin.png[align="center",title="Plugin Install"]
+{nbsp} +
+
+//ST: !
+
+When it’s installed, you should be able to see or display the ** Multimode Network Transformation** tab.
+
+//ST: !
+
+Then, load the graph and :
+
+* Click on **Load Attributes** and select **Type** on **Attribute Type.**
+* For left matrix select **Administrator – Company** and right matrix **Company – Adminstrator**
+* Enable **Remove Edges** and** Remove Nodes**, and Click** Run
+
+//ST: !
+
+> The plugin will modify directly the graph on the current workspace. If you want to keep the original graph, be sure
+> you have copied your workspace.
+
+//ST: !
+
+image::en/k-partite/k-partite-config.png[align="center",title="MultiPartite_Gephi"]
+{nbsp} +
+
+
+//ST: !
+
+The plugins will actually to transform each relation like:
+
+(Administrator)**<-**(Company)**->**(Administrator)
+
+to
+
+(Administrator)**<-[**{weight=Nb of Common Companies}**]->**(Administrator)
+
+Now you should have a graph with only administrators on the graph preview.
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-p2p.png[align="center",title="Tuto_Multi_P2P"]
+{nbsp} +
+
+
+//ST: !
+
+To generate the relation between companies, replay the same steps
+from the original graph (duplicate the workspace0 again) and in the **Multimode Network Transformation** use :
+
+* Left Matrix : Company – Administrator
+* Right Matrix : Administrator – Company
+
+//ST: !
+
+image::en/k-partite/k-partite-config-2.png[align="center",title="MultiPartite_Gephi_2"]
+{nbsp} +
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-c2c.png[align="center",title="Tuto_Multi_C2C"]
+{nbsp} +
+
+//ST: !
+
+With the 2 new graphs, and playing with the weighted degree of the nodes, we are able to see some particularities:
+
+* The maximum of **common administrator** between company is **3**: Bouygues and Alstom with Olivier Bouygues, Patrick Kron, Georges Chodron de Courcel
+* **Half of the companies** are sharing at least 2 administrators.
+* The maximum of **common company** between person is **3**: Gerard Lamarche and Paul Demaray Jr see each other in Total, GDF Suez and Lafarge SA.
+* From all administrators that are in at least 2 companies,** 1/3 of them are at least in the _same_ 2 companies**.
+
+//ST: !
+
+Theses statements could have been deduced from the original graph, but now, the information is more visible and accessible, especially if you want to share it to a large public.
+
+//ST: !
+
+> The next steps are now mostly to look at graphs, and summarize all
+> the particularities into some kind of output (poster, interactive graph,
+> newspaper etc…)
+
+== Limitations
+
+//ST: Limitations
+
+The plugin works quite well, but sometimes it has some limitation on large graphs. Or sometimes you may want to reduce your graph and have a custom metric computed.
+Then the only alternative to that is to use a script that will generate the graph for you.
+
+== Benefits
+
+//ST: Conclusion
+
+Multipartite graphs are complex to analyse, but synthesizing them to lower partite graphs gives you a better vision and insights about your data.
+
+== The end
+
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_slides.md b/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_slides.md
new file mode 100644
index 0000000..05b4b9d
--- /dev/null
+++ b/docs/generated-pdf/subdir/k-partite-bi-partite-graph-en_temp_slides.md
@@ -0,0 +1,262 @@
+= K-partite and Bipartite Graph (Multimode Networks Transformations)
+Matthieu Totet
+2017-01-29
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Author and documentation
+
+== !
+
+The Multimode Networks Transformations plugin allows you transform a k-partite graph into a mono-partite graph.
+
+== !
+
+Official page of the plugin: https://gephi.org/plugins/#/plugin/semantic[https://gephi.org/plugins/#/plugin/semantic]
+
+Plugin Author: https://github.com/jaroslav-kuchar[Jaroslav Kuchar]
+
+Plugin sources: https://github.com/totetmatt/gephi-plugins/tree/multimode-network[https://github.com/totetmatt/gephi-plugins/tree/multimode-network]
+
+Licensed under: GNU GENERAL PUBLIC LICENSE
+
+
+
+== K-partite, Bipartite … What is it ?
+
+Most of the time, when you create a graph, all the nodes are representing the same « kind » of object.
+
+* In a Social Network: Persons
+* In a Route Network: Bus Stops, Airports, Stations etc…
+* In a Geographical Network: Countries
+
+== !
+
+But sometimes, you will encounter graphs that will have 2, 3 or multiple « kinds » of nodes where the links are almost exclusively going from one type to the other.
+These
+graphs are called **Multipartite Graphs** :
+
+== !
+
+* If you have **2** kinds of nodes, it’s a **Bi**partite Graph (or **2**-partite)
+** Example : Social Network : Persons < - > Companies
+
+== !
+
+* If you have **3** kinds of nodes it’s a** 3**-partite Graph
+** Example : Movie Network : Actors < - > Movies < - > Movie Companies
+
+== !
+
+* If you have **k** kinds of nodes it’s a **k**-partite Graph
+
+== !
+
+If you want a proper scientific definition, you can check the https://en.wikipedia.org/wiki/Multipartite_graph[Wikipedia] page about the subject.
+
+
+== The dataset
+
+We will use a well known data set : The Cac 40 Administrators relationship from http://www.citoyennumerique.fr/?tag=cac40[www.citoyennumerique.fr.]
+
+The files for Gephi are accessible here : link::../../resources/k-partite-bi-partite.zip[Gexf files]
+
+The tutorial will be based on the **SimpleGraph**, but you also have access to the **FullGraph** to play with.
+
+== !
+
+The CAC 40 represents the 40 most valuable company on the french stock market. A **company** has several **administrators ** and nothing prevent an **administrator** to be in multiple **companies**. So the graph is build such as **companies** are sources of edges and targets are the **administrators**.
+
+== !
+
+This is a typical bi-partite graph. If you look on the data, part of the node has a property type **Administrator** and the other part has a property type **Company**.
+
+[.stretch]
+image::en/k-partite/k-partite-simple-graph.png[align="center",title="Cac 40 bipartite graph"]
+
+
+
+== !
+
+We can get quick observations:
+
+* Filtering by InDegree, we can check that Michel Pebereau and Jean-Martin Folz are in **5 companies** at the same time.
+* Filtering by OutDegree, we can check that St-Gobain and Total have **8 administrators** that are in other companies.
+
+But that could be done without graph, an ordered list on excel would have done the job.
+
+
+== !
+
+Having a k-partite graph makes somehow the graph unfriendly to read. Usually, the metrics will be difficult to interpret, and
+generating a good visualisation from it won't be trivial.
+
+One method here is to **reduce** the **bi-partite** graph into a **mono-partite** graph.
+
+== !
+
+
+> By reducing, we will « lose » information but we gain in **readability** and **synthesis**,
+> that’s part of the work in data visualisation to see at which level we
+> want to go and how to deal with this trade of information. This will
+> mostly be driven by the question you want to answer.
+
+== !
+
+The principle of the reduction is to consider a type of **nodes** as **edges** and build a new graph based on this rule.
+In our example we have 2 ways to go:
+
+* Either we decide that the nodes of type « Company » are the edges. It will generate a "**graph** of **Persons **"
+* Either we decide that the nodes of type « Person » are the edges. It will generate a "**graph** of **Company **"
+
+== !
+
+> Here both graphs generated makes « sense » (network of
+> entities) but it might appear that in some other case, one of the generated graphs won't really be 'interesting'.
+> That’s part of your work to judge this aspect.
+
+Now, how to perform the transformation?
+
+
+== Using a Plugin
+
+== !
+
+The easiest way is to use the link::https://marketplace.gephi.org/plugin/multimode-networks-transformations-2/[Multimode Network Transformation plugin].
+The plugin is available on the Gephi app market and in the plugin manager in Gephi.
+
+== !
+
+[.stretch]
+image::en/k-partite/k-partite-install-plugin.png[align="center",title="Plugin Install"]
+
+
+== !
+
+When it’s installed, you should be able to see or display the ** Multimode Network Transformation** tab.
+
+== !
+
+Then, load the graph and :
+
+* Click on **Load Attributes** and select **Type** on **Attribute Type.**
+* For left matrix select **Administrator – Company** and right matrix **Company – Adminstrator**
+* Enable **Remove Edges** and** Remove Nodes**, and Click** Run
+
+== !
+
+> The plugin will modify directly the graph on the current workspace. If you want to keep the original graph, be sure
+> you have copied your workspace.
+
+== !
+
+[.stretch]
+image::en/k-partite/k-partite-config.png[align="center",title="MultiPartite_Gephi"]
+
+
+
+== !
+
+The plugins will actually to transform each relation like:
+
+(Administrator)**<-**(Company)**->**(Administrator)
+
+to
+
+(Administrator)**<-[**{weight=Nb of Common Companies}**]->**(Administrator)
+
+Now you should have a graph with only administrators on the graph preview.
+
+== !
+
+[.stretch]
+image::en/k-partite/k-partite-simple-p2p.png[align="center",title="Tuto_Multi_P2P"]
+
+
+
+== !
+
+To generate the relation between companies, replay the same steps
+from the original graph (duplicate the workspace0 again) and in the **Multimode Network Transformation** use :
+
+* Left Matrix : Company – Administrator
+* Right Matrix : Administrator – Company
+
+== !
+
+[.stretch]
+image::en/k-partite/k-partite-config-2.png[align="center",title="MultiPartite_Gephi_2"]
+
+
+== !
+
+[.stretch]
+image::en/k-partite/k-partite-simple-c2c.png[align="center",title="Tuto_Multi_C2C"]
+
+
+== !
+
+With the 2 new graphs, and playing with the weighted degree of the nodes, we are able to see some particularities:
+
+* The maximum of **common administrator** between company is **3**: Bouygues and Alstom with Olivier Bouygues, Patrick Kron, Georges Chodron de Courcel
+* **Half of the companies** are sharing at least 2 administrators.
+* The maximum of **common company** between person is **3**: Gerard Lamarche and Paul Demaray Jr see each other in Total, GDF Suez and Lafarge SA.
+* From all administrators that are in at least 2 companies,** 1/3 of them are at least in the _same_ 2 companies**.
+
+== !
+
+Theses statements could have been deduced from the original graph, but now, the information is more visible and accessible, especially if you want to share it to a large public.
+
+== !
+
+> The next steps are now mostly to look at graphs, and summarize all
+> the particularities into some kind of output (poster, interactive graph,
+> newspaper etc…)
+
+
+== Limitations
+
+The plugin works quite well, but sometimes it has some limitation on large graphs. Or sometimes you may want to reduce your graph and have a custom metric computed.
+Then the only alternative to that is to use a script that will generate the graph for you.
+
+
+== Conclusion
+
+Multipartite graphs are complex to analyse, but synthesizing them to lower partite graphs gives you a better vision and insights about your data.
+
+
+== The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_common.md b/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_common.md
new file mode 100644
index 0000000..dcdc73f
--- /dev/null
+++ b/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_common.md
@@ -0,0 +1,216 @@
+= K-partite and Bipartite Graph
+Matthieu Totet
+2017-01-29
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== K-partite, Bipartite … What is it ?
+
+//ST: K-partite, Bipartite … What is it ?
+
+Most of the time, when you create a graph, all the nodes are representing the same « kind » of object.
+
+* In a Social Network : Persons
+* In a Route Netowork : Bus Stops, Airports, Stations etc…
+* In a Geographical Network : Countries
+
+//ST: !
+
+But sometimes, you will encounter graphs that will have 2, 3 or multiple « kinds » of nodes where the links are almost exclusively going from one type to the other. These
+graphs are called **Multipartite Graphs** :
+
+//ST: !
+
+* If you have **2** kinds of nodes, it’s a **Bi**partite Graph (or **2**-partite)
+** Example : Social Network : Persons < - > Companies
+
+//ST: !
+
+
+* If you have **3** kinds of nodes it’s a** 3**-partite Graph
+** Example : Movie Network : Actors < - > Movies < - > Movie Companies
+
+//ST: !
+
+* If you have **k** kinds of nodes it’s a **k**-partite Graph
+
+//ST: !
+
+If you want a proper scientific definition, you can check the https://en.wikipedia.org/wiki/Multipartite_graph[Wikipedia] page about the subject.
+
+== The Dataset
+
+//ST: The dataset
+
+We will use a well known data set : The Cac 40 Administrators relationship from http://www.citoyennumerique.fr/?tag=cac40[www.citoyennumerique.fr.]
+
+The files for Gephi are accessible here : link::../../resources/k-partite-bi-partite.zip[Gexf files]
+
+The tutorial will be based on the **SimpleGraph**, but you also have access to the **FullGraph** to play with.
+
+//ST: !
+
+The CAC 40 represents the 40 most valuable company on the french stock market. A **company** has several **administrators ** and nothing prevent an **administrator** to be in multiple **companies**. So the graph is build such as **companies** are sources of edges and targets are the **administrators**.
+
+//ST: !
+
+This is a typical bi-partite graph. If you look on the data, part of the node has a property type **Administrator** and the other part has a property type **Company**.
+
+image::en/k-partite/k-partite-simple-graph.png[Cac 40 bipartite graph]
+
+
+//ST: !
+
+We can get quick observations:
+
+* Filtering by InDegree, we can check that Michel Pebereau and Jean-Martin Folz are in **5 companies** at the same time.
+* Filtering by OutDegree, we can check that St-Gobain and Total have **8 administrators** that are in other companies.
+
+But that could be done without graph, an ordered list on excel would have done the job.
+
+== How to deal with multipartite graphs ?
+
+//ST:!
+
+Having a k-partite graph makes somehow the graph unfriendly to read. Usually, the metrics will be difficult to interpret, and
+generating a good visualisation from it won't be trivial.
+
+One method here is to **reduce** the **bi-partite** graph into a **mono-partite** graph.
+
+//ST: !
+
+
+> By reducing, we will « lose » information but we gain in **readability** and **synthesis**,
+> that’s part of the work in data visualisation to see at which level we
+> want to go and how to deal with this trade of information. This will
+> mostly be driven by the question you want to answer.
+
+//ST: !
+
+The principle of the reduction is to consider a type of **nodes** as **edges** and build a new graph based on this rule.
+In our example we have 2 ways to go :
+
+* Either we decide that the nodes of type « Company » are the edges. It will generate a "**graph** of **Persons **"
+* Either we decide that the nodes of type « Person » are the edges. It will generate a "**graph** of **Company **"
+
+//ST: !
+
+> Here both graphs generated makes « sense » (network of
+> entities) but it might appears that in some other case, one of the generated graph won't really be 'interesting'.
+> That’s part of your work to judge this aspect.
+
+Now, how to perform the transformation ?
+
+== Using a Plugin
+
+//ST: Using a Plugin
+
+
+//ST: !
+
+The easiest way is to use the link::https://marketplace.gephi.org/plugin/multimode-networks-transformations-2/[Multimode Network Transformation plugin].
+The plugin is available on the Gephi app market and in the plugin manager in Gephi.
+
+//ST: !
+
+image::en/k-partite/k-partite-install-plugin.png[Plugin Install]
+
+//ST: !
+
+When it’s installed, you should be able to see or display the ** Multimode Network Transformation** tab.
+
+//ST: !
+
+Then, load the graph and :
+
+* Click on **Load Attributes** and select **Type** on **Attribute Type.**
+* For left matrix select **Administrator – Company** and right matrix **Company – Adminstrator**
+* Enable **Remove Edges** and** Remove Nodes**, and Click** Run
+
+//ST: !
+
+> The plugin will modify directly the graph on the current workspace. If you want' to keep the original graph, be sure
+> you've done a clone of your workspace.
+
+//ST: !
+
+image::en/k-partite/k-partite-config.png[MultiPartite_Gephi]
+
+
+//ST: !
+
+The plugins will actually to transform each relation like:
+
+(Administrator)**<-**(Company)**->**(Administrator)
+
+to
+
+(Administrator)**<-[**{weight=Nb of Common Companies}**]->**(Administrator)
+
+Now you should have a graph with only administrators on the graph preview.
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-p2p.png[Tuto_Multi_P2P]
+
+
+//ST: !
+
+To generate the relation between companies, replay the same steps
+from the original graph (duplicate the workspace0 again) and in the **Multimode Network Transformation** use :
+
+* Left Matrix : Company – Administrator
+* Right Matrix : Administrator – Company
+
+//ST: !
+
+image::en/k-partite/k-partite-config-2.png[MultiPartite_Gephi_2]
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-c2c.png[Tuto_Multi_C2C]
+
+//ST: !
+
+With the 2 new graphs, and playing with the weighted degree of the nodes, we are able to see some particularities:
+
+* The maximum of** common administrator** between company is **3** : Bouygues and Alstom with Olivier Bouygues, Patrick Kron, Georges Chodron de Courcel
+* **Half of the companies** are sharing at least 2 administrators .
+* The maximum of **common company** between person is **3**
+ : Gerard Lamarche and Paul Demaray Jr see each other when Total, GDF
+
+//ST: !
+
+Suez and Lafarge SA have a meeting with Board of Directors.
+* From all administrators that are in at least 2 companies,** 1/3 of them are at least in the _same_ 2 companies**.
+
+Theses statements could have been deduced from the original graph, but now, the information is more visible and accessible, especially if you want to share it to a large public.
+
+//ST: !
+
+> The works now is mostly to look at graphs, and resume all
+> the particularities within a production (poster, interactive graph,
+> newspaper etc…)
+
+== Limitations
+
+//ST: Limitations
+
+The plugin works quite well, but sometime it has some limitation on large graph. Or sometime you may want to reduce your graph and have a custom metric computed. Then the only alternative to that is to use a script that will generate you the graph.
+
+== Conclusion
+
+//ST: Conclusion
+
+Multipartite graph are complex to analyse, but synthesising it to lower partite graphs gives you more vision to get insight about your data.
diff --git a/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_html.md b/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_html.md
new file mode 100644
index 0000000..dcdc73f
--- /dev/null
+++ b/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_html.md
@@ -0,0 +1,216 @@
+= K-partite and Bipartite Graph
+Matthieu Totet
+2017-01-29
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== K-partite, Bipartite … What is it ?
+
+//ST: K-partite, Bipartite … What is it ?
+
+Most of the time, when you create a graph, all the nodes are representing the same « kind » of object.
+
+* In a Social Network : Persons
+* In a Route Netowork : Bus Stops, Airports, Stations etc…
+* In a Geographical Network : Countries
+
+//ST: !
+
+But sometimes, you will encounter graphs that will have 2, 3 or multiple « kinds » of nodes where the links are almost exclusively going from one type to the other. These
+graphs are called **Multipartite Graphs** :
+
+//ST: !
+
+* If you have **2** kinds of nodes, it’s a **Bi**partite Graph (or **2**-partite)
+** Example : Social Network : Persons < - > Companies
+
+//ST: !
+
+
+* If you have **3** kinds of nodes it’s a** 3**-partite Graph
+** Example : Movie Network : Actors < - > Movies < - > Movie Companies
+
+//ST: !
+
+* If you have **k** kinds of nodes it’s a **k**-partite Graph
+
+//ST: !
+
+If you want a proper scientific definition, you can check the https://en.wikipedia.org/wiki/Multipartite_graph[Wikipedia] page about the subject.
+
+== The Dataset
+
+//ST: The dataset
+
+We will use a well known data set : The Cac 40 Administrators relationship from http://www.citoyennumerique.fr/?tag=cac40[www.citoyennumerique.fr.]
+
+The files for Gephi are accessible here : link::../../resources/k-partite-bi-partite.zip[Gexf files]
+
+The tutorial will be based on the **SimpleGraph**, but you also have access to the **FullGraph** to play with.
+
+//ST: !
+
+The CAC 40 represents the 40 most valuable company on the french stock market. A **company** has several **administrators ** and nothing prevent an **administrator** to be in multiple **companies**. So the graph is build such as **companies** are sources of edges and targets are the **administrators**.
+
+//ST: !
+
+This is a typical bi-partite graph. If you look on the data, part of the node has a property type **Administrator** and the other part has a property type **Company**.
+
+image::en/k-partite/k-partite-simple-graph.png[Cac 40 bipartite graph]
+
+
+//ST: !
+
+We can get quick observations:
+
+* Filtering by InDegree, we can check that Michel Pebereau and Jean-Martin Folz are in **5 companies** at the same time.
+* Filtering by OutDegree, we can check that St-Gobain and Total have **8 administrators** that are in other companies.
+
+But that could be done without graph, an ordered list on excel would have done the job.
+
+== How to deal with multipartite graphs ?
+
+//ST:!
+
+Having a k-partite graph makes somehow the graph unfriendly to read. Usually, the metrics will be difficult to interpret, and
+generating a good visualisation from it won't be trivial.
+
+One method here is to **reduce** the **bi-partite** graph into a **mono-partite** graph.
+
+//ST: !
+
+
+> By reducing, we will « lose » information but we gain in **readability** and **synthesis**,
+> that’s part of the work in data visualisation to see at which level we
+> want to go and how to deal with this trade of information. This will
+> mostly be driven by the question you want to answer.
+
+//ST: !
+
+The principle of the reduction is to consider a type of **nodes** as **edges** and build a new graph based on this rule.
+In our example we have 2 ways to go :
+
+* Either we decide that the nodes of type « Company » are the edges. It will generate a "**graph** of **Persons **"
+* Either we decide that the nodes of type « Person » are the edges. It will generate a "**graph** of **Company **"
+
+//ST: !
+
+> Here both graphs generated makes « sense » (network of
+> entities) but it might appears that in some other case, one of the generated graph won't really be 'interesting'.
+> That’s part of your work to judge this aspect.
+
+Now, how to perform the transformation ?
+
+== Using a Plugin
+
+//ST: Using a Plugin
+
+
+//ST: !
+
+The easiest way is to use the link::https://marketplace.gephi.org/plugin/multimode-networks-transformations-2/[Multimode Network Transformation plugin].
+The plugin is available on the Gephi app market and in the plugin manager in Gephi.
+
+//ST: !
+
+image::en/k-partite/k-partite-install-plugin.png[Plugin Install]
+
+//ST: !
+
+When it’s installed, you should be able to see or display the ** Multimode Network Transformation** tab.
+
+//ST: !
+
+Then, load the graph and :
+
+* Click on **Load Attributes** and select **Type** on **Attribute Type.**
+* For left matrix select **Administrator – Company** and right matrix **Company – Adminstrator**
+* Enable **Remove Edges** and** Remove Nodes**, and Click** Run
+
+//ST: !
+
+> The plugin will modify directly the graph on the current workspace. If you want' to keep the original graph, be sure
+> you've done a clone of your workspace.
+
+//ST: !
+
+image::en/k-partite/k-partite-config.png[MultiPartite_Gephi]
+
+
+//ST: !
+
+The plugins will actually to transform each relation like:
+
+(Administrator)**<-**(Company)**->**(Administrator)
+
+to
+
+(Administrator)**<-[**{weight=Nb of Common Companies}**]->**(Administrator)
+
+Now you should have a graph with only administrators on the graph preview.
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-p2p.png[Tuto_Multi_P2P]
+
+
+//ST: !
+
+To generate the relation between companies, replay the same steps
+from the original graph (duplicate the workspace0 again) and in the **Multimode Network Transformation** use :
+
+* Left Matrix : Company – Administrator
+* Right Matrix : Administrator – Company
+
+//ST: !
+
+image::en/k-partite/k-partite-config-2.png[MultiPartite_Gephi_2]
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-c2c.png[Tuto_Multi_C2C]
+
+//ST: !
+
+With the 2 new graphs, and playing with the weighted degree of the nodes, we are able to see some particularities:
+
+* The maximum of** common administrator** between company is **3** : Bouygues and Alstom with Olivier Bouygues, Patrick Kron, Georges Chodron de Courcel
+* **Half of the companies** are sharing at least 2 administrators .
+* The maximum of **common company** between person is **3**
+ : Gerard Lamarche and Paul Demaray Jr see each other when Total, GDF
+
+//ST: !
+
+Suez and Lafarge SA have a meeting with Board of Directors.
+* From all administrators that are in at least 2 companies,** 1/3 of them are at least in the _same_ 2 companies**.
+
+Theses statements could have been deduced from the original graph, but now, the information is more visible and accessible, especially if you want to share it to a large public.
+
+//ST: !
+
+> The works now is mostly to look at graphs, and resume all
+> the particularities within a production (poster, interactive graph,
+> newspaper etc…)
+
+== Limitations
+
+//ST: Limitations
+
+The plugin works quite well, but sometime it has some limitation on large graph. Or sometime you may want to reduce your graph and have a custom metric computed. Then the only alternative to that is to use a script that will generate you the graph.
+
+== Conclusion
+
+//ST: Conclusion
+
+Multipartite graph are complex to analyse, but synthesising it to lower partite graphs gives you more vision to get insight about your data.
diff --git a/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_pdf.md b/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_pdf.md
new file mode 100644
index 0000000..dcdc73f
--- /dev/null
+++ b/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_pdf.md
@@ -0,0 +1,216 @@
+= K-partite and Bipartite Graph
+Matthieu Totet
+2017-01-29
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== K-partite, Bipartite … What is it ?
+
+//ST: K-partite, Bipartite … What is it ?
+
+Most of the time, when you create a graph, all the nodes are representing the same « kind » of object.
+
+* In a Social Network : Persons
+* In a Route Netowork : Bus Stops, Airports, Stations etc…
+* In a Geographical Network : Countries
+
+//ST: !
+
+But sometimes, you will encounter graphs that will have 2, 3 or multiple « kinds » of nodes where the links are almost exclusively going from one type to the other. These
+graphs are called **Multipartite Graphs** :
+
+//ST: !
+
+* If you have **2** kinds of nodes, it’s a **Bi**partite Graph (or **2**-partite)
+** Example : Social Network : Persons < - > Companies
+
+//ST: !
+
+
+* If you have **3** kinds of nodes it’s a** 3**-partite Graph
+** Example : Movie Network : Actors < - > Movies < - > Movie Companies
+
+//ST: !
+
+* If you have **k** kinds of nodes it’s a **k**-partite Graph
+
+//ST: !
+
+If you want a proper scientific definition, you can check the https://en.wikipedia.org/wiki/Multipartite_graph[Wikipedia] page about the subject.
+
+== The Dataset
+
+//ST: The dataset
+
+We will use a well known data set : The Cac 40 Administrators relationship from http://www.citoyennumerique.fr/?tag=cac40[www.citoyennumerique.fr.]
+
+The files for Gephi are accessible here : link::../../resources/k-partite-bi-partite.zip[Gexf files]
+
+The tutorial will be based on the **SimpleGraph**, but you also have access to the **FullGraph** to play with.
+
+//ST: !
+
+The CAC 40 represents the 40 most valuable company on the french stock market. A **company** has several **administrators ** and nothing prevent an **administrator** to be in multiple **companies**. So the graph is build such as **companies** are sources of edges and targets are the **administrators**.
+
+//ST: !
+
+This is a typical bi-partite graph. If you look on the data, part of the node has a property type **Administrator** and the other part has a property type **Company**.
+
+image::en/k-partite/k-partite-simple-graph.png[Cac 40 bipartite graph]
+
+
+//ST: !
+
+We can get quick observations:
+
+* Filtering by InDegree, we can check that Michel Pebereau and Jean-Martin Folz are in **5 companies** at the same time.
+* Filtering by OutDegree, we can check that St-Gobain and Total have **8 administrators** that are in other companies.
+
+But that could be done without graph, an ordered list on excel would have done the job.
+
+== How to deal with multipartite graphs ?
+
+//ST:!
+
+Having a k-partite graph makes somehow the graph unfriendly to read. Usually, the metrics will be difficult to interpret, and
+generating a good visualisation from it won't be trivial.
+
+One method here is to **reduce** the **bi-partite** graph into a **mono-partite** graph.
+
+//ST: !
+
+
+> By reducing, we will « lose » information but we gain in **readability** and **synthesis**,
+> that’s part of the work in data visualisation to see at which level we
+> want to go and how to deal with this trade of information. This will
+> mostly be driven by the question you want to answer.
+
+//ST: !
+
+The principle of the reduction is to consider a type of **nodes** as **edges** and build a new graph based on this rule.
+In our example we have 2 ways to go :
+
+* Either we decide that the nodes of type « Company » are the edges. It will generate a "**graph** of **Persons **"
+* Either we decide that the nodes of type « Person » are the edges. It will generate a "**graph** of **Company **"
+
+//ST: !
+
+> Here both graphs generated makes « sense » (network of
+> entities) but it might appears that in some other case, one of the generated graph won't really be 'interesting'.
+> That’s part of your work to judge this aspect.
+
+Now, how to perform the transformation ?
+
+== Using a Plugin
+
+//ST: Using a Plugin
+
+
+//ST: !
+
+The easiest way is to use the link::https://marketplace.gephi.org/plugin/multimode-networks-transformations-2/[Multimode Network Transformation plugin].
+The plugin is available on the Gephi app market and in the plugin manager in Gephi.
+
+//ST: !
+
+image::en/k-partite/k-partite-install-plugin.png[Plugin Install]
+
+//ST: !
+
+When it’s installed, you should be able to see or display the ** Multimode Network Transformation** tab.
+
+//ST: !
+
+Then, load the graph and :
+
+* Click on **Load Attributes** and select **Type** on **Attribute Type.**
+* For left matrix select **Administrator – Company** and right matrix **Company – Adminstrator**
+* Enable **Remove Edges** and** Remove Nodes**, and Click** Run
+
+//ST: !
+
+> The plugin will modify directly the graph on the current workspace. If you want' to keep the original graph, be sure
+> you've done a clone of your workspace.
+
+//ST: !
+
+image::en/k-partite/k-partite-config.png[MultiPartite_Gephi]
+
+
+//ST: !
+
+The plugins will actually to transform each relation like:
+
+(Administrator)**<-**(Company)**->**(Administrator)
+
+to
+
+(Administrator)**<-[**{weight=Nb of Common Companies}**]->**(Administrator)
+
+Now you should have a graph with only administrators on the graph preview.
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-p2p.png[Tuto_Multi_P2P]
+
+
+//ST: !
+
+To generate the relation between companies, replay the same steps
+from the original graph (duplicate the workspace0 again) and in the **Multimode Network Transformation** use :
+
+* Left Matrix : Company – Administrator
+* Right Matrix : Administrator – Company
+
+//ST: !
+
+image::en/k-partite/k-partite-config-2.png[MultiPartite_Gephi_2]
+
+//ST: !
+
+image::en/k-partite/k-partite-simple-c2c.png[Tuto_Multi_C2C]
+
+//ST: !
+
+With the 2 new graphs, and playing with the weighted degree of the nodes, we are able to see some particularities:
+
+* The maximum of** common administrator** between company is **3** : Bouygues and Alstom with Olivier Bouygues, Patrick Kron, Georges Chodron de Courcel
+* **Half of the companies** are sharing at least 2 administrators .
+* The maximum of **common company** between person is **3**
+ : Gerard Lamarche and Paul Demaray Jr see each other when Total, GDF
+
+//ST: !
+
+Suez and Lafarge SA have a meeting with Board of Directors.
+* From all administrators that are in at least 2 companies,** 1/3 of them are at least in the _same_ 2 companies**.
+
+Theses statements could have been deduced from the original graph, but now, the information is more visible and accessible, especially if you want to share it to a large public.
+
+//ST: !
+
+> The works now is mostly to look at graphs, and resume all
+> the particularities within a production (poster, interactive graph,
+> newspaper etc…)
+
+== Limitations
+
+//ST: Limitations
+
+The plugin works quite well, but sometime it has some limitation on large graph. Or sometime you may want to reduce your graph and have a custom metric computed. Then the only alternative to that is to use a script that will generate you the graph.
+
+== Conclusion
+
+//ST: Conclusion
+
+Multipartite graph are complex to analyse, but synthesising it to lower partite graphs gives you more vision to get insight about your data.
diff --git a/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_slides.md b/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_slides.md
new file mode 100644
index 0000000..3ff235d
--- /dev/null
+++ b/docs/generated-pdf/subdir/k-partite-bi-partite-graph_temp_slides.md
@@ -0,0 +1,209 @@
+= K-partite and Bipartite Graph
+Matthieu Totet
+2017-01-29
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+
+== K-partite, Bipartite … What is it ?
+
+Most of the time, when you create a graph, all the nodes are representing the same « kind » of object.
+
+* In a Social Network : Persons
+* In a Route Netowork : Bus Stops, Airports, Stations etc…
+* In a Geographical Network : Countries
+
+== !
+
+But sometimes, you will encounter graphs that will have 2, 3 or multiple « kinds » of nodes where the links are almost exclusively going from one type to the other. These
+graphs are called **Multipartite Graphs** :
+
+== !
+
+* If you have **2** kinds of nodes, it’s a **Bi**partite Graph (or **2**-partite)
+** Example : Social Network : Persons < - > Companies
+
+== !
+
+
+* If you have **3** kinds of nodes it’s a** 3**-partite Graph
+** Example : Movie Network : Actors < - > Movies < - > Movie Companies
+
+== !
+
+* If you have **k** kinds of nodes it’s a **k**-partite Graph
+
+== !
+
+If you want a proper scientific definition, you can check the https://en.wikipedia.org/wiki/Multipartite_graph[Wikipedia] page about the subject.
+
+
+== The dataset
+
+We will use a well known data set : The Cac 40 Administrators relationship from http://www.citoyennumerique.fr/?tag=cac40[www.citoyennumerique.fr.]
+
+The files for Gephi are accessible here : link::../../resources/k-partite-bi-partite.zip[Gexf files]
+
+The tutorial will be based on the **SimpleGraph**, but you also have access to the **FullGraph** to play with.
+
+== !
+
+The CAC 40 represents the 40 most valuable company on the french stock market. A **company** has several **administrators ** and nothing prevent an **administrator** to be in multiple **companies**. So the graph is build such as **companies** are sources of edges and targets are the **administrators**.
+
+== !
+
+This is a typical bi-partite graph. If you look on the data, part of the node has a property type **Administrator** and the other part has a property type **Company**.
+
+image::en/k-partite/k-partite-simple-graph.png[Cac 40 bipartite graph]
+
+
+== !
+
+We can get quick observations:
+
+* Filtering by InDegree, we can check that Michel Pebereau and Jean-Martin Folz are in **5 companies** at the same time.
+* Filtering by OutDegree, we can check that St-Gobain and Total have **8 administrators** that are in other companies.
+
+But that could be done without graph, an ordered list on excel would have done the job.
+
+
+//ST:!
+
+Having a k-partite graph makes somehow the graph unfriendly to read. Usually, the metrics will be difficult to interpret, and
+generating a good visualisation from it won't be trivial.
+
+One method here is to **reduce** the **bi-partite** graph into a **mono-partite** graph.
+
+== !
+
+
+> By reducing, we will « lose » information but we gain in **readability** and **synthesis**,
+> that’s part of the work in data visualisation to see at which level we
+> want to go and how to deal with this trade of information. This will
+> mostly be driven by the question you want to answer.
+
+== !
+
+The principle of the reduction is to consider a type of **nodes** as **edges** and build a new graph based on this rule.
+In our example we have 2 ways to go :
+
+* Either we decide that the nodes of type « Company » are the edges. It will generate a "**graph** of **Persons **"
+* Either we decide that the nodes of type « Person » are the edges. It will generate a "**graph** of **Company **"
+
+== !
+
+> Here both graphs generated makes « sense » (network of
+> entities) but it might appears that in some other case, one of the generated graph won't really be 'interesting'.
+> That’s part of your work to judge this aspect.
+
+Now, how to perform the transformation ?
+
+
+== Using a Plugin
+
+
+== !
+
+The easiest way is to use the link::https://marketplace.gephi.org/plugin/multimode-networks-transformations-2/[Multimode Network Transformation plugin].
+The plugin is available on the Gephi app market and in the plugin manager in Gephi.
+
+== !
+
+image::en/k-partite/k-partite-install-plugin.png[Plugin Install]
+
+== !
+
+When it’s installed, you should be able to see or display the ** Multimode Network Transformation** tab.
+
+== !
+
+Then, load the graph and :
+
+* Click on **Load Attributes** and select **Type** on **Attribute Type.**
+* For left matrix select **Administrator – Company** and right matrix **Company – Adminstrator**
+* Enable **Remove Edges** and** Remove Nodes**, and Click** Run
+
+== !
+
+> The plugin will modify directly the graph on the current workspace. If you want' to keep the original graph, be sure
+> you've done a clone of your workspace.
+
+== !
+
+image::en/k-partite/k-partite-config.png[MultiPartite_Gephi]
+
+
+== !
+
+The plugins will actually to transform each relation like:
+
+(Administrator)**<-**(Company)**->**(Administrator)
+
+to
+
+(Administrator)**<-[**{weight=Nb of Common Companies}**]->**(Administrator)
+
+Now you should have a graph with only administrators on the graph preview.
+
+== !
+
+image::en/k-partite/k-partite-simple-p2p.png[Tuto_Multi_P2P]
+
+
+== !
+
+To generate the relation between companies, replay the same steps
+from the original graph (duplicate the workspace0 again) and in the **Multimode Network Transformation** use :
+
+* Left Matrix : Company – Administrator
+* Right Matrix : Administrator – Company
+
+== !
+
+image::en/k-partite/k-partite-config-2.png[MultiPartite_Gephi_2]
+
+== !
+
+image::en/k-partite/k-partite-simple-c2c.png[Tuto_Multi_C2C]
+
+== !
+
+With the 2 new graphs, and playing with the weighted degree of the nodes, we are able to see some particularities:
+
+* The maximum of** common administrator** between company is **3** : Bouygues and Alstom with Olivier Bouygues, Patrick Kron, Georges Chodron de Courcel
+* **Half of the companies** are sharing at least 2 administrators .
+* The maximum of **common company** between person is **3**
+ : Gerard Lamarche and Paul Demaray Jr see each other when Total, GDF
+
+== !
+
+Suez and Lafarge SA have a meeting with Board of Directors.
+* From all administrators that are in at least 2 companies,** 1/3 of them are at least in the _same_ 2 companies**.
+
+Theses statements could have been deduced from the original graph, but now, the information is more visible and accessible, especially if you want to share it to a large public.
+
+== !
+
+> The works now is mostly to look at graphs, and resume all
+> the particularities within a production (poster, interactive graph,
+> newspaper etc…)
+
+
+== Limitations
+
+The plugin works quite well, but sometime it has some limitation on large graph. Or sometime you may want to reduce your graph and have a custom metric computed. Then the only alternative to that is to use a script that will generate you the graph.
+
+
+== Conclusion
+
+Multipartite graph are complex to analyse, but synthesising it to lower partite graphs gives you more vision to get insight about your data.
diff --git a/docs/generated-pdf/subdir/semantic-web-importer-en_temp_common.md b/docs/generated-pdf/subdir/semantic-web-importer-en_temp_common.md
new file mode 100644
index 0000000..d421e13
--- /dev/null
+++ b/docs/generated-pdf/subdir/semantic-web-importer-en_temp_common.md
@@ -0,0 +1,213 @@
+= Semantic Web Importer
+Matthieu Totet
+2017-01-31
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+//ST: Author and documentation
+== Author and documentation
+
+//ST: !
+
+The Semantic Web Importer plugin allows you to query a SPARQL endpoint and represent the result as a graph in Gephi.
+
+//ST: !
+
+Official page of the plugin: https://gephi.org/plugins/#/plugin/semantic
+
+Plugin Author: https://github.com/ErwanDemairy[Erwan Demairy]
+
+Plugin sources: https://scm.gforge.inria.fr/anonscm/git/segviz-public/gephi-plugins.git/[git://scm.gforge.inria.fr/segviz-public/gephi-plugins.git]
+
+Licensed under: Cecill-C
+
+
+//ST: Introduction
+== Introduction to concepts
+
+RDF stands for https://en.wikipedia.org/wiki/Resource_Description_Framework[Resource Description Framework] .
+It is a way to represent information of all kinds.
+It is now a specification and since 1995 people are working on RDF and all subjects related to information representation for automatic system.
+
+RDF can be considered as the « Grand Father » of graph representation.
+
+//ST: !
+The idea is very simple, all information can be represented as a triplet :
+
+> `(subject, predicate, object)`
+
+//ST: !
+
+To query such a dataset, a query language has been developed: https://en.wikipedia.org/wiki/SPARQL[SPARQL]
+
+The SPARQL format is close to SQL format and tries to be as close as possible to a natural language.
+
+http://dbpedia.org[DBpedia] is the RDF version of Wikipedia. It is an open datastore that tries to gather and offers in RDF format all information that you can find in wikipedia or other knowledge service.
+
+//ST: !
+
+DBPedia is very interesting because it preformats concepts and makes them available for automatic processes or complex queries across concepts.
+
+You can query it through a web interface or via any RDF toolkit :
+
+http://dbpedia.org/sparql[http://dbpedia.org/sparql]
+
+[source,SQL]
+----
+select *
+where
+{
+ ?predicate ?object.
+}
+----
+
+//ST: Quickstart
+== Quickstart
+
+The result of a SPARQL can be a table as we could do it with SQL, but you can also build a graph based on your query.
+The Semantic Web Plugin uses this feature to draw the result of your query into Gephi.
+
+First, we need to install Semantic web import. You can find and install this plugin in the menu Tools -> Plugins of Gephi.
+
+When installed, go to the Semantic Web Import tab.
+
+//ST: !
+
+In the *Driver* tab, choose *Remote - REST endpoint* and put *http://dbpedia.org/sparql* and *EndPoint URL*.
+
+[IMPORTANT]
+====
+Press *Enter* after typing the URL endpoint so the url is getting validated and used.
+====
+
+//ST: !
+
+image::en/semantic-web-importer/setup_1.png[align="center", title="Setup endpoint"]
+{nbsp} +
+
+//ST: !
+
+Go to *Query* tab, remove the dummy query and put this one:
+```
+CONSTRUCT{
+ ?person "person".
+ ?movie "movie".
+ ?movie ?person .
+}
+WHERE
+{
+ ?movie a .
+ ?movie ?rel1 .
+ ?movie ?rel2 ?person.
+ ?person a .
+}
+```
+//ST: !
+
+Finally, click on *Run*
+
+//ST: !
+
+image::en/semantic-web-importer/setup_2.png[align="center", title="SPARQL Query"]
+{nbsp} +
+
+//ST: !
+
+Go back to the Graph preview and you should see a graph. Use the layout tab to get a more intuitive rendering.
+
+image::en/semantic-web-importer/result_1.png[align="center", title"result"]
+{nbsp} +
+
+//ST: Details
+== Details
+
+//ST: !
+
+==== CONSTRUCT block
+
+The `CONSTRUCT` block define the new graph to build from the query. That's the description of your data the plugin will interpet
+to build the graph.
+
+What is the syntax for this `CONSTRUCT` block :
+
+//ST: !
+
+- `?a ?b`
+
+-> Will create the node `?a` with the property `nodeProperty` equal to `?b`.
+
+- `?a ( or ?c) ?b`
+
+-> will create a link between `?a` and `?b` with label ``
+
+//ST: !
+
+==== WHERE block
+The `WHERE` block define the data to match based on the query typed in :
+
+//ST: !
+
+- `?movie a .`
+
+-> Get all subject which is a ``.
+The `a` is actually a SPARQL special word for ``.
+So it’s exactly equal to `?movie `
+
+- `?movie ?rel1 .`
+
+-> Match all subjects which have any predicate where the object is ``
+
+//ST: !
+
+- `?movie ?rel2 ?person.`
+
+-> Match any triplet
+
+- `?person a .`
+
+-> Match the subject `?person` if it's a ``.
+
+//ST: !
+
+In other words, match the variable `?movie` to any entity that is a `Film` and that has at least 1 triplet where `Leonardo` is an object. Match also all `?person` that is a `Person`.
+
+The result in our case is actually a bi-modal graph with Movies and Persons shared with Leonardo Dicaprio.
+
+//ST Tips to go further
+== Tips to go further
+
+What is interesting with this method is the way to query the data, and the fact that it is easily extensible to other subjects just by changing few things in the query.
+We can think about Music Band and Player relationships, the traditionnal Persons to Companies, or more originally, all the common places where your favorites movies where shot, etc.
+The limit is your imagination to graph.
+
+//ST: !
+
+On our example, you can run multiple time the same query and change which person you want to « expand », it will automatically create the global network of all your research.
+It will also maybe give you some insight on which new movies to watch tonight 😉
+
+But keep in mind that you are heavily dependent on Dbpedia data quality (or other RDF entrypoint), which can alterate a lot you research.
+
+//ST: !
+
+If you want more refined queries, you will have to learn SPARQL and RDF in more details. There are a lot of tutorials around the web that will help you to master SPARQL.
+
+== The end
+
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/semantic-web-importer-en_temp_html.md b/docs/generated-pdf/subdir/semantic-web-importer-en_temp_html.md
new file mode 100644
index 0000000..7167b56
--- /dev/null
+++ b/docs/generated-pdf/subdir/semantic-web-importer-en_temp_html.md
@@ -0,0 +1,230 @@
+= Semantic Web Importer
+Matthieu Totet
+2017-01-31
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+//ST: Author and documentation
+== Author and documentation
+
+//ST: !
+
+The Semantic Web Importer plugin allows you to query a SPARQL endpoint and represent the result as a graph in Gephi.
+
+//ST: !
+
+Official page of the plugin: https://gephi.org/plugins/#/plugin/semantic
+
+Plugin Author: https://github.com/ErwanDemairy[Erwan Demairy]
+
+Plugin sources: https://scm.gforge.inria.fr/anonscm/git/segviz-public/gephi-plugins.git/[git://scm.gforge.inria.fr/segviz-public/gephi-plugins.git]
+
+Licensed under: Cecill-C
+
+
+//ST: Introduction
+== Introduction to concepts
+
+RDF stands for https://en.wikipedia.org/wiki/Resource_Description_Framework[Resource Description Framework] .
+It is a way to represent information of all kinds.
+It is now a specification and since 1995 people are working on RDF and all subjects related to information representation for automatic system.
+
+RDF can be considered as the « Grand Father » of graph representation.
+
+//ST: !
+The idea is very simple, all information can be represented as a triplet :
+
+> `(subject, predicate, object)`
+
+//ST: !
+
+To query such a dataset, a query language has been developed: https://en.wikipedia.org/wiki/SPARQL[SPARQL]
+
+The SPARQL format is close to SQL format and tries to be as close as possible to a natural language.
+
+http://dbpedia.org[DBpedia] is the RDF version of Wikipedia. It is an open datastore that tries to gather and offers in RDF format all information that you can find in wikipedia or other knowledge service.
+
+//ST: !
+
+DBPedia is very interesting because it preformats concepts and makes them available for automatic processes or complex queries across concepts.
+
+You can query it through a web interface or via any RDF toolkit :
+
+http://dbpedia.org/sparql[http://dbpedia.org/sparql]
+
+[source,SQL]
+----
+select *
+where
+{
+ ?predicate ?object.
+}
+----
+
+//ST: Quickstart
+== Quickstart
+
+The result of a SPARQL can be a table as we could do it with SQL, but you can also build a graph based on your query.
+The Semantic Web Plugin uses this feature to draw the result of your query into Gephi.
+
+First, we need to install Semantic web import. You can find and install this plugin in the menu Tools -> Plugins of Gephi.
+
+When installed, go to the Semantic Web Import tab.
+
+//ST: !
+
+In the *Driver* tab, choose *Remote - REST endpoint* and put *http://dbpedia.org/sparql* and *EndPoint URL*.
+
+[IMPORTANT]
+====
+Press *Enter* after typing the URL endpoint so the url is getting validated and used.
+====
+
+//ST: !
+
+image::en/semantic-web-importer/setup_1.png[align="center", title="Setup endpoint"]
+{nbsp} +
+
+//ST: !
+
+Go to *Query* tab, remove the dummy query and put this one:
+```
+CONSTRUCT{
+ ?person "person".
+ ?movie "movie".
+ ?movie ?person .
+}
+WHERE
+{
+ ?movie a .
+ ?movie ?rel1 .
+ ?movie ?rel2 ?person.
+ ?person a .
+}
+```
+//ST: !
+
+Finally, click on *Run*
+
+//ST: !
+
+image::en/semantic-web-importer/setup_2.png[align="center", title="SPARQL Query"]
+{nbsp} +
+
+//ST: !
+
+Go back to the Graph preview and you should see a graph. Use the layout tab to get a more intuitive rendering.
+
+image::en/semantic-web-importer/result_1.png[align="center", title"result"]
+{nbsp} +
+
+//ST: Details
+== Details
+
+//ST: !
+
+==== CONSTRUCT block
+
+The `CONSTRUCT` block define the new graph to build from the query. That's the description of your data the plugin will interpet
+to build the graph.
+
+What is the syntax for this `CONSTRUCT` block :
+
+//ST: !
+
+- `?a ?b`
+
+-> Will create the node `?a` with the property `nodeProperty` equal to `?b`.
+
+- `?a ( or ?c) ?b`
+
+-> will create a link between `?a` and `?b` with label ``
+
+//ST: !
+
+==== WHERE block
+The `WHERE` block define the data to match based on the query typed in :
+
+//ST: !
+
+- `?movie a .`
+
+-> Get all subject which is a ``.
+The `a` is actually a SPARQL special word for ``.
+So it’s exactly equal to `?movie `
+
+- `?movie ?rel1 .`
+
+-> Match all subjects which have any predicate where the object is ``
+
+//ST: !
+
+- `?movie ?rel2 ?person.`
+
+-> Match any triplet
+
+- `?person a .`
+
+-> Match the subject `?person` if it's a ``.
+
+//ST: !
+
+In other words, match the variable `?movie` to any entity that is a `Film` and that has at least 1 triplet where `Leonardo` is an object. Match also all `?person` that is a `Person`.
+
+The result in our case is actually a bi-modal graph with Movies and Persons shared with Leonardo Dicaprio.
+
+//ST Tips to go further
+== Tips to go further
+
+What is interesting with this method is the way to query the data, and the fact that it is easily extensible to other subjects just by changing few things in the query.
+We can think about Music Band and Player relationships, the traditionnal Persons to Companies, or more originally, all the common places where your favorites movies where shot, etc.
+The limit is your imagination to graph.
+
+//ST: !
+
+On our example, you can run multiple time the same query and change which person you want to « expand », it will automatically create the global network of all your research.
+It will also maybe give you some insight on which new movies to watch tonight 😉
+
+But keep in mind that you are heavily dependent on Dbpedia data quality (or other RDF entrypoint), which can alterate a lot you research.
+
+//ST: !
+
+If you want more refined queries, you will have to learn SPARQL and RDF in more details. There are a lot of tutorials around the web that will help you to master SPARQL.
+
+== The end
+
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/semantic-web-importer-en_temp_pdf.md b/docs/generated-pdf/subdir/semantic-web-importer-en_temp_pdf.md
new file mode 100644
index 0000000..d421e13
--- /dev/null
+++ b/docs/generated-pdf/subdir/semantic-web-importer-en_temp_pdf.md
@@ -0,0 +1,213 @@
+= Semantic Web Importer
+Matthieu Totet
+2017-01-31
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+//ST: Author and documentation
+== Author and documentation
+
+//ST: !
+
+The Semantic Web Importer plugin allows you to query a SPARQL endpoint and represent the result as a graph in Gephi.
+
+//ST: !
+
+Official page of the plugin: https://gephi.org/plugins/#/plugin/semantic
+
+Plugin Author: https://github.com/ErwanDemairy[Erwan Demairy]
+
+Plugin sources: https://scm.gforge.inria.fr/anonscm/git/segviz-public/gephi-plugins.git/[git://scm.gforge.inria.fr/segviz-public/gephi-plugins.git]
+
+Licensed under: Cecill-C
+
+
+//ST: Introduction
+== Introduction to concepts
+
+RDF stands for https://en.wikipedia.org/wiki/Resource_Description_Framework[Resource Description Framework] .
+It is a way to represent information of all kinds.
+It is now a specification and since 1995 people are working on RDF and all subjects related to information representation for automatic system.
+
+RDF can be considered as the « Grand Father » of graph representation.
+
+//ST: !
+The idea is very simple, all information can be represented as a triplet :
+
+> `(subject, predicate, object)`
+
+//ST: !
+
+To query such a dataset, a query language has been developed: https://en.wikipedia.org/wiki/SPARQL[SPARQL]
+
+The SPARQL format is close to SQL format and tries to be as close as possible to a natural language.
+
+http://dbpedia.org[DBpedia] is the RDF version of Wikipedia. It is an open datastore that tries to gather and offers in RDF format all information that you can find in wikipedia or other knowledge service.
+
+//ST: !
+
+DBPedia is very interesting because it preformats concepts and makes them available for automatic processes or complex queries across concepts.
+
+You can query it through a web interface or via any RDF toolkit :
+
+http://dbpedia.org/sparql[http://dbpedia.org/sparql]
+
+[source,SQL]
+----
+select *
+where
+{
+ ?predicate ?object.
+}
+----
+
+//ST: Quickstart
+== Quickstart
+
+The result of a SPARQL can be a table as we could do it with SQL, but you can also build a graph based on your query.
+The Semantic Web Plugin uses this feature to draw the result of your query into Gephi.
+
+First, we need to install Semantic web import. You can find and install this plugin in the menu Tools -> Plugins of Gephi.
+
+When installed, go to the Semantic Web Import tab.
+
+//ST: !
+
+In the *Driver* tab, choose *Remote - REST endpoint* and put *http://dbpedia.org/sparql* and *EndPoint URL*.
+
+[IMPORTANT]
+====
+Press *Enter* after typing the URL endpoint so the url is getting validated and used.
+====
+
+//ST: !
+
+image::en/semantic-web-importer/setup_1.png[align="center", title="Setup endpoint"]
+{nbsp} +
+
+//ST: !
+
+Go to *Query* tab, remove the dummy query and put this one:
+```
+CONSTRUCT{
+ ?person "person".
+ ?movie "movie".
+ ?movie ?person .
+}
+WHERE
+{
+ ?movie a .
+ ?movie ?rel1 .
+ ?movie ?rel2 ?person.
+ ?person a .
+}
+```
+//ST: !
+
+Finally, click on *Run*
+
+//ST: !
+
+image::en/semantic-web-importer/setup_2.png[align="center", title="SPARQL Query"]
+{nbsp} +
+
+//ST: !
+
+Go back to the Graph preview and you should see a graph. Use the layout tab to get a more intuitive rendering.
+
+image::en/semantic-web-importer/result_1.png[align="center", title"result"]
+{nbsp} +
+
+//ST: Details
+== Details
+
+//ST: !
+
+==== CONSTRUCT block
+
+The `CONSTRUCT` block define the new graph to build from the query. That's the description of your data the plugin will interpet
+to build the graph.
+
+What is the syntax for this `CONSTRUCT` block :
+
+//ST: !
+
+- `?a ?b`
+
+-> Will create the node `?a` with the property `nodeProperty` equal to `?b`.
+
+- `?a ( or ?c) ?b`
+
+-> will create a link between `?a` and `?b` with label ``
+
+//ST: !
+
+==== WHERE block
+The `WHERE` block define the data to match based on the query typed in :
+
+//ST: !
+
+- `?movie a .`
+
+-> Get all subject which is a ``.
+The `a` is actually a SPARQL special word for ``.
+So it’s exactly equal to `?movie `
+
+- `?movie ?rel1 .`
+
+-> Match all subjects which have any predicate where the object is ``
+
+//ST: !
+
+- `?movie ?rel2 ?person.`
+
+-> Match any triplet
+
+- `?person a .`
+
+-> Match the subject `?person` if it's a ``.
+
+//ST: !
+
+In other words, match the variable `?movie` to any entity that is a `Film` and that has at least 1 triplet where `Leonardo` is an object. Match also all `?person` that is a `Person`.
+
+The result in our case is actually a bi-modal graph with Movies and Persons shared with Leonardo Dicaprio.
+
+//ST Tips to go further
+== Tips to go further
+
+What is interesting with this method is the way to query the data, and the fact that it is easily extensible to other subjects just by changing few things in the query.
+We can think about Music Band and Player relationships, the traditionnal Persons to Companies, or more originally, all the common places where your favorites movies where shot, etc.
+The limit is your imagination to graph.
+
+//ST: !
+
+On our example, you can run multiple time the same query and change which person you want to « expand », it will automatically create the global network of all your research.
+It will also maybe give you some insight on which new movies to watch tonight 😉
+
+But keep in mind that you are heavily dependent on Dbpedia data quality (or other RDF entrypoint), which can alterate a lot you research.
+
+//ST: !
+
+If you want more refined queries, you will have to learn SPARQL and RDF in more details. There are a lot of tutorials around the web that will help you to master SPARQL.
+
+== The end
+
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/semantic-web-importer-en_temp_slides.md b/docs/generated-pdf/subdir/semantic-web-importer-en_temp_slides.md
new file mode 100644
index 0000000..fe9108a
--- /dev/null
+++ b/docs/generated-pdf/subdir/semantic-web-importer-en_temp_slides.md
@@ -0,0 +1,227 @@
+= Semantic Web Importer
+Matthieu Totet
+2017-01-31
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Author and documentation
+
+== !
+
+The Semantic Web Importer plugin allows you to query a SPARQL endpoint and represent the result as a graph in Gephi.
+
+== !
+
+Official page of the plugin: https://gephi.org/plugins/#/plugin/semantic
+
+Plugin Author: https://github.com/ErwanDemairy[Erwan Demairy]
+
+Plugin sources: https://scm.gforge.inria.fr/anonscm/git/segviz-public/gephi-plugins.git/[git://scm.gforge.inria.fr/segviz-public/gephi-plugins.git]
+
+Licensed under: Cecill-C
+
+
+== Introduction
+
+RDF stands for https://en.wikipedia.org/wiki/Resource_Description_Framework[Resource Description Framework] .
+It is a way to represent information of all kinds.
+It is now a specification and since 1995 people are working on RDF and all subjects related to information representation for automatic system.
+
+RDF can be considered as the « Grand Father » of graph representation.
+
+== !
+The idea is very simple, all information can be represented as a triplet :
+
+> `(subject, predicate, object)`
+
+== !
+
+To query such a dataset, a query language has been developed: https://en.wikipedia.org/wiki/SPARQL[SPARQL]
+
+The SPARQL format is close to SQL format and tries to be as close as possible to a natural language.
+
+http://dbpedia.org[DBpedia] is the RDF version of Wikipedia. It is an open datastore that tries to gather and offers in RDF format all information that you can find in wikipedia or other knowledge service.
+
+== !
+
+DBPedia is very interesting because it preformats concepts and makes them available for automatic processes or complex queries across concepts.
+
+You can query it through a web interface or via any RDF toolkit :
+
+http://dbpedia.org/sparql[http://dbpedia.org/sparql]
+
+[source,SQL]
+----
+select *
+where
+{
+ ?predicate ?object.
+}
+----
+
+== Quickstart
+
+The result of a SPARQL can be a table as we could do it with SQL, but you can also build a graph based on your query.
+The Semantic Web Plugin uses this feature to draw the result of your query into Gephi.
+
+First, we need to install Semantic web import. You can find and install this plugin in the menu Tools -> Plugins of Gephi.
+
+When installed, go to the Semantic Web Import tab.
+
+== !
+
+In the *Driver* tab, choose *Remote - REST endpoint* and put *http://dbpedia.org/sparql* and *EndPoint URL*.
+
+[IMPORTANT]
+====
+Press *Enter* after typing the URL endpoint so the url is getting validated and used.
+====
+
+== !
+
+[.stretch]
+image::en/semantic-web-importer/setup_1.png[align="center", title="Setup endpoint"]
+
+
+== !
+
+Go to *Query* tab, remove the dummy query and put this one:
+```
+CONSTRUCT{
+ ?person "person".
+ ?movie "movie".
+ ?movie ?person .
+}
+WHERE
+{
+ ?movie a .
+ ?movie ?rel1 .
+ ?movie ?rel2 ?person.
+ ?person a .
+}
+```
+== !
+
+Finally, click on *Run*
+
+== !
+
+[.stretch]
+image::en/semantic-web-importer/setup_2.png[align="center", title="SPARQL Query"]
+
+
+== !
+
+Go back to the Graph preview and you should see a graph. Use the layout tab to get a more intuitive rendering.
+
+[.stretch]
+image::en/semantic-web-importer/result_1.png[align="center", title"result"]
+
+
+== Details
+
+== !
+
+==== CONSTRUCT block
+
+The `CONSTRUCT` block define the new graph to build from the query. That's the description of your data the plugin will interpet
+to build the graph.
+
+What is the syntax for this `CONSTRUCT` block :
+
+== !
+
+- `?a ?b`
+
+-> Will create the node `?a` with the property `nodeProperty` equal to `?b`.
+
+- `?a ( or ?c) ?b`
+
+-> will create a link between `?a` and `?b` with label ``
+
+== !
+
+==== WHERE block
+The `WHERE` block define the data to match based on the query typed in :
+
+== !
+
+- `?movie a .`
+
+-> Get all subject which is a ``.
+The `a` is actually a SPARQL special word for ``.
+So it’s exactly equal to `?movie `
+
+- `?movie ?rel1 .`
+
+-> Match all subjects which have any predicate where the object is ``
+
+== !
+
+- `?movie ?rel2 ?person.`
+
+-> Match any triplet
+
+- `?person a .`
+
+-> Match the subject `?person` if it's a ``.
+
+== !
+
+In other words, match the variable `?movie` to any entity that is a `Film` and that has at least 1 triplet where `Leonardo` is an object. Match also all `?person` that is a `Person`.
+
+The result in our case is actually a bi-modal graph with Movies and Persons shared with Leonardo Dicaprio.
+
+//ST Tips to go further
+
+What is interesting with this method is the way to query the data, and the fact that it is easily extensible to other subjects just by changing few things in the query.
+We can think about Music Band and Player relationships, the traditionnal Persons to Companies, or more originally, all the common places where your favorites movies where shot, etc.
+The limit is your imagination to graph.
+
+== !
+
+On our example, you can run multiple time the same query and change which person you want to « expand », it will automatically create the global network of all your research.
+It will also maybe give you some insight on which new movies to watch tonight 😉
+
+But keep in mind that you are heavily dependent on Dbpedia data quality (or other RDF entrypoint), which can alterate a lot you research.
+
+== !
+
+If you want more refined queries, you will have to learn SPARQL and RDF in more details. There are a lot of tutorials around the web that will help you to master SPARQL.
+
+
+== The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/similarity-computer-en_temp_common.md b/docs/generated-pdf/subdir/similarity-computer-en_temp_common.md
new file mode 100644
index 0000000..7d0de4a
--- /dev/null
+++ b/docs/generated-pdf/subdir/similarity-computer-en_temp_common.md
@@ -0,0 +1,163 @@
+= Creating a network from a table of entities and their attributes
+Clément Levallois
+2017-02-22
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+//ST: Presentation of the plugin
+
+//ST: !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+//ST: !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, nodes are the entities listed in column A
+* Nodes' attributes must be listed in columns B, C, D, etc.
+* Connections will be created between nodes, when they have identical attributes.
+* Attributes can have values, stored in columns right next to the attribute.
+
+//ST: !
+==== 1. The input
+//ST: !
+
+image::An-Excel-file.png[align="center", title="An Excel file"]
+{nbsp} +
+
+
+//ST: !
+==== 2. The output
+//ST: !
+
+image::en/similarity-computer/result.png[align="center", title="Resulting network"]
+{nbsp} +
+
+== Installing the plugin
+//ST: Installing the plugin
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Opening the plugin
+//ST: Opening the plugin
+//ST: !
+
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+{nbsp} +
+
+== Using the plugin
+//ST: Using the plugin
+
+//ST: !
+==== First panel
+//ST: !
+
+image::Select-a-file.png[align="center", title="Select a file"]
+{nbsp} +
+
+//ST: Is your file with a header?
+
+//ST: !
+
+image::en/similarity-computer/file-without-header-en.png[align="center", title="A file without headers"]
+{nbsp} +
+
+//ST: !
+
+image::en/similarity-computer/file-with-header-en.png[align="center", title="A file with headers"]
+{nbsp} +
+
+//ST: !
+==== Second panel
+//ST: !
+
+image::en/similarity-computer/plugin-4-en.png[align="center", title="Parameter for weight"]
+{nbsp} +
+
+//ST: !
+==== Third panel
+//ST: !
+
+image::en/similarity-computer/plugin-5-en.png[align="center", title="Confirmation panel"]
+{nbsp} +
+
+== How is the similarity computed, exactly?
+//ST: How is the similarity computed, exactly?
+//ST: !
+
+We use the cosine similarity. Sounds complicated, but it is not. http://stackoverflow.com/questions/1746501/can-someone-give-an-example-of-cosine-similarity-in-a-very-simple-graphical-wa[Check here].
+
+The source code for the cosine calculation is in this https://github.com/gephi/gephi-plugins/blob/master-forge/modules/SimilarityComputer/src/main/java/net/clementlevallois/computer/CosineCalculation.java[file], at https://github.com/gephi/gephi-plugins/blob/master-forge/modules/SimilarityComputer/src/main/java/net/clementlevallois/computer/CosineCalculation.java#L110[this place].
+
+== FAQ / special notes on the plugin
+//ST: FAQ / special notes on the plugin
+//ST: !
+
+==== 1. Excel files should be .xlsx, not .xls
+
+Because they represent two slightly different files formats, and the plugin supports only .xlsx
+
+//ST: !
+
+==== 2. csv files are ok.
+
+If you select a http://www.computerhope.com/issues/ch001356.htm[csv file], you will be asked to indicate the field delimiter and optionally the text delimiter.
+
+image::en/similarity-computer/plugin-6-en.png[align="center", title="When a csv file is selected"]
+{nbsp} +
+
+//ST: !
+
+==== 3. You can't use numerical values in the attributes
+
+image::en/similarity-computer/numerical-attributes-en.png[align="center", title="Age is a numerical attribute"]
+{nbsp} +
+
+This is too bad. If there is enough demand for it I'll add this feature, which is not trivial.
+
+//ST: !
+
+==== 4. Each entity should appear only on one line
+
+image::en/similarity-computer/plugin-7-en.png[align="center", title="An entity appearing twice"]
+{nbsp} +
+
+David appears on lines 2 and 5 (because he made two purchases). Only the latest line where David appears (line 5) will be taken into account.
+
+== The end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/similarity-computer-en_temp_html.md b/docs/generated-pdf/subdir/similarity-computer-en_temp_html.md
new file mode 100644
index 0000000..d8e2bed
--- /dev/null
+++ b/docs/generated-pdf/subdir/similarity-computer-en_temp_html.md
@@ -0,0 +1,180 @@
+= Creating a network from a table of entities and their attributes
+Clément Levallois
+2017-02-22
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+//ST: Presentation of the plugin
+
+//ST: !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+//ST: !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, nodes are the entities listed in column A
+* Nodes' attributes must be listed in columns B, C, D, etc.
+* Connections will be created between nodes, when they have identical attributes.
+* Attributes can have values, stored in columns right next to the attribute.
+
+//ST: !
+==== 1. The input
+//ST: !
+
+image::An-Excel-file.png[align="center", title="An Excel file"]
+{nbsp} +
+
+
+//ST: !
+==== 2. The output
+//ST: !
+
+image::en/similarity-computer/result.png[align="center", title="Resulting network"]
+{nbsp} +
+
+== Installing the plugin
+//ST: Installing the plugin
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Opening the plugin
+//ST: Opening the plugin
+//ST: !
+
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+{nbsp} +
+
+== Using the plugin
+//ST: Using the plugin
+
+//ST: !
+==== First panel
+//ST: !
+
+image::Select-a-file.png[align="center", title="Select a file"]
+{nbsp} +
+
+//ST: Is your file with a header?
+
+//ST: !
+
+image::en/similarity-computer/file-without-header-en.png[align="center", title="A file without headers"]
+{nbsp} +
+
+//ST: !
+
+image::en/similarity-computer/file-with-header-en.png[align="center", title="A file with headers"]
+{nbsp} +
+
+//ST: !
+==== Second panel
+//ST: !
+
+image::en/similarity-computer/plugin-4-en.png[align="center", title="Parameter for weight"]
+{nbsp} +
+
+//ST: !
+==== Third panel
+//ST: !
+
+image::en/similarity-computer/plugin-5-en.png[align="center", title="Confirmation panel"]
+{nbsp} +
+
+== How is the similarity computed, exactly?
+//ST: How is the similarity computed, exactly?
+//ST: !
+
+We use the cosine similarity. Sounds complicated, but it is not. http://stackoverflow.com/questions/1746501/can-someone-give-an-example-of-cosine-similarity-in-a-very-simple-graphical-wa[Check here].
+
+The source code for the cosine calculation is in this https://github.com/gephi/gephi-plugins/blob/master-forge/modules/SimilarityComputer/src/main/java/net/clementlevallois/computer/CosineCalculation.java[file], at https://github.com/gephi/gephi-plugins/blob/master-forge/modules/SimilarityComputer/src/main/java/net/clementlevallois/computer/CosineCalculation.java#L110[this place].
+
+== FAQ / special notes on the plugin
+//ST: FAQ / special notes on the plugin
+//ST: !
+
+==== 1. Excel files should be .xlsx, not .xls
+
+Because they represent two slightly different files formats, and the plugin supports only .xlsx
+
+//ST: !
+
+==== 2. csv files are ok.
+
+If you select a http://www.computerhope.com/issues/ch001356.htm[csv file], you will be asked to indicate the field delimiter and optionally the text delimiter.
+
+image::en/similarity-computer/plugin-6-en.png[align="center", title="When a csv file is selected"]
+{nbsp} +
+
+//ST: !
+
+==== 3. You can't use numerical values in the attributes
+
+image::en/similarity-computer/numerical-attributes-en.png[align="center", title="Age is a numerical attribute"]
+{nbsp} +
+
+This is too bad. If there is enough demand for it I'll add this feature, which is not trivial.
+
+//ST: !
+
+==== 4. Each entity should appear only on one line
+
+image::en/similarity-computer/plugin-7-en.png[align="center", title="An entity appearing twice"]
+{nbsp} +
+
+David appears on lines 2 and 5 (because he made two purchases). Only the latest line where David appears (line 5) will be taken into account.
+
+== The end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/similarity-computer-en_temp_pdf.md b/docs/generated-pdf/subdir/similarity-computer-en_temp_pdf.md
new file mode 100644
index 0000000..7d0de4a
--- /dev/null
+++ b/docs/generated-pdf/subdir/similarity-computer-en_temp_pdf.md
@@ -0,0 +1,163 @@
+= Creating a network from a table of entities and their attributes
+Clément Levallois
+2017-02-22
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+//ST: Presentation of the plugin
+
+//ST: !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+//ST: !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, nodes are the entities listed in column A
+* Nodes' attributes must be listed in columns B, C, D, etc.
+* Connections will be created between nodes, when they have identical attributes.
+* Attributes can have values, stored in columns right next to the attribute.
+
+//ST: !
+==== 1. The input
+//ST: !
+
+image::An-Excel-file.png[align="center", title="An Excel file"]
+{nbsp} +
+
+
+//ST: !
+==== 2. The output
+//ST: !
+
+image::en/similarity-computer/result.png[align="center", title="Resulting network"]
+{nbsp} +
+
+== Installing the plugin
+//ST: Installing the plugin
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Opening the plugin
+//ST: Opening the plugin
+//ST: !
+
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+{nbsp} +
+
+== Using the plugin
+//ST: Using the plugin
+
+//ST: !
+==== First panel
+//ST: !
+
+image::Select-a-file.png[align="center", title="Select a file"]
+{nbsp} +
+
+//ST: Is your file with a header?
+
+//ST: !
+
+image::en/similarity-computer/file-without-header-en.png[align="center", title="A file without headers"]
+{nbsp} +
+
+//ST: !
+
+image::en/similarity-computer/file-with-header-en.png[align="center", title="A file with headers"]
+{nbsp} +
+
+//ST: !
+==== Second panel
+//ST: !
+
+image::en/similarity-computer/plugin-4-en.png[align="center", title="Parameter for weight"]
+{nbsp} +
+
+//ST: !
+==== Third panel
+//ST: !
+
+image::en/similarity-computer/plugin-5-en.png[align="center", title="Confirmation panel"]
+{nbsp} +
+
+== How is the similarity computed, exactly?
+//ST: How is the similarity computed, exactly?
+//ST: !
+
+We use the cosine similarity. Sounds complicated, but it is not. http://stackoverflow.com/questions/1746501/can-someone-give-an-example-of-cosine-similarity-in-a-very-simple-graphical-wa[Check here].
+
+The source code for the cosine calculation is in this https://github.com/gephi/gephi-plugins/blob/master-forge/modules/SimilarityComputer/src/main/java/net/clementlevallois/computer/CosineCalculation.java[file], at https://github.com/gephi/gephi-plugins/blob/master-forge/modules/SimilarityComputer/src/main/java/net/clementlevallois/computer/CosineCalculation.java#L110[this place].
+
+== FAQ / special notes on the plugin
+//ST: FAQ / special notes on the plugin
+//ST: !
+
+==== 1. Excel files should be .xlsx, not .xls
+
+Because they represent two slightly different files formats, and the plugin supports only .xlsx
+
+//ST: !
+
+==== 2. csv files are ok.
+
+If you select a http://www.computerhope.com/issues/ch001356.htm[csv file], you will be asked to indicate the field delimiter and optionally the text delimiter.
+
+image::en/similarity-computer/plugin-6-en.png[align="center", title="When a csv file is selected"]
+{nbsp} +
+
+//ST: !
+
+==== 3. You can't use numerical values in the attributes
+
+image::en/similarity-computer/numerical-attributes-en.png[align="center", title="Age is a numerical attribute"]
+{nbsp} +
+
+This is too bad. If there is enough demand for it I'll add this feature, which is not trivial.
+
+//ST: !
+
+==== 4. Each entity should appear only on one line
+
+image::en/similarity-computer/plugin-7-en.png[align="center", title="An entity appearing twice"]
+{nbsp} +
+
+David appears on lines 2 and 5 (because he made two purchases). Only the latest line where David appears (line 5) will be taken into account.
+
+== The end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/similarity-computer-en_temp_slides.md b/docs/generated-pdf/subdir/similarity-computer-en_temp_slides.md
new file mode 100644
index 0000000..9d9da0f
--- /dev/null
+++ b/docs/generated-pdf/subdir/similarity-computer-en_temp_slides.md
@@ -0,0 +1,187 @@
+= Creating a network from a table of entities and their attributes
+Clément Levallois
+2017-02-22
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Presentation of the plugin
+
+== !
+
+This plugin is created by https://www.clementlevallois.net[Clement Levallois].
+
+It converts a spreadsheet or a csv file into a network.
+
+== !
+
+This plugin enables you to:
+
+* Start from a data table in Excel or csv format
+* In the data table, nodes are the entities listed in column A
+* Nodes' attributes must be listed in columns B, C, D, etc.
+* Connections will be created between nodes, when they have identical attributes.
+* Attributes can have values, stored in columns right next to the attribute.
+
+== !
+==== 1. The input
+== !
+
+[.stretch]
+image::An-Excel-file.png[align="center", title="An Excel file"]
+
+
+
+== !
+==== 2. The output
+== !
+
+[.stretch]
+image::en/similarity-computer/result.png[align="center", title="Resulting network"]
+
+
+== Installing the plugin
+== !
+
+[.stretch]
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+
+
+== !
+
+[.stretch]
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+
+
+== !
+
+[.stretch]
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+
+
+== Opening the plugin
+== !
+
+[.stretch]
+image::Open-the-plugin-via-the-menu-File---Import.png[align="center", title="Open the plugin via the menu File - Import"]
+
+
+== Using the plugin
+
+== !
+==== First panel
+== !
+
+[.stretch]
+image::Select-a-file.png[align="center", title="Select a file"]
+
+
+== Is your file with a header?
+
+== !
+
+[.stretch]
+image::en/similarity-computer/file-without-header-en.png[align="center", title="A file without headers"]
+
+
+== !
+
+[.stretch]
+image::en/similarity-computer/file-with-header-en.png[align="center", title="A file with headers"]
+
+
+== !
+==== Second panel
+== !
+
+[.stretch]
+image::en/similarity-computer/plugin-4-en.png[align="center", title="Parameter for weight"]
+
+
+== !
+==== Third panel
+== !
+
+[.stretch]
+image::en/similarity-computer/plugin-5-en.png[align="center", title="Confirmation panel"]
+
+
+== How is the similarity computed, exactly?
+== !
+
+We use the cosine similarity. Sounds complicated, but it is not. http://stackoverflow.com/questions/1746501/can-someone-give-an-example-of-cosine-similarity-in-a-very-simple-graphical-wa[Check here].
+
+The source code for the cosine calculation is in this https://github.com/gephi/gephi-plugins/blob/master-forge/modules/SimilarityComputer/src/main/java/net/clementlevallois/computer/CosineCalculation.java[file], at https://github.com/gephi/gephi-plugins/blob/master-forge/modules/SimilarityComputer/src/main/java/net/clementlevallois/computer/CosineCalculation.java#L110[this place].
+
+== FAQ / special notes on the plugin
+== !
+
+==== 1. Excel files should be .xlsx, not .xls
+
+Because they represent two slightly different files formats, and the plugin supports only .xlsx
+
+== !
+
+==== 2. csv files are ok.
+
+If you select a http://www.computerhope.com/issues/ch001356.htm[csv file], you will be asked to indicate the field delimiter and optionally the text delimiter.
+
+[.stretch]
+image::en/similarity-computer/plugin-6-en.png[align="center", title="When a csv file is selected"]
+
+
+== !
+
+==== 3. You can't use numerical values in the attributes
+
+[.stretch]
+image::en/similarity-computer/numerical-attributes-en.png[align="center", title="Age is a numerical attribute"]
+
+
+This is too bad. If there is enough demand for it I'll add this feature, which is not trivial.
+
+== !
+
+==== 4. Each entity should appear only on one line
+
+[.stretch]
+image::en/similarity-computer/plugin-7-en.png[align="center", title="An entity appearing twice"]
+
+
+David appears on lines 2 and 5 (because he made two purchases). Only the latest line where David appears (line 5) will be taken into account.
+
+== The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_common.md b/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_common.md
new file mode 100644
index 0000000..c8aae23
--- /dev/null
+++ b/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_common.md
@@ -0,0 +1,474 @@
+= Simple Gephi Project from A to Z
+Clément Levallois
+2017-01-20
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+
+== Description of the project
+
+//ST: Description of the project
+//ST: !
+
+
+This project is for complete beginners to Gephi.
+It supposes you have Gephi installed and running on your computer. That is all.
+
+When finishing this tutorial, you should be able to:
+[options="compact"]
+- be familiar with the vocabulary to discuss networks
+- download a network file for this exercise
+- description of the file / the network
+//ST: !
+- open a network file
+- read the report after opening a file
+- show the labels of the nodes
+- layout the network
+//ST: !
+- visualize attributes of the network
+- prettify the network for enhanced readability
+- compute the centrality of the nodes in the network
+- visualize attributes created by Gephi
+- export a visualization as a picture or pdf
+
+
+== be familiar with the terminology to discuss networks
+//ST: terminology to discuss networks
+//ST: !
+image::terminology-for-networks.png[align="center",title="terminology for networks"]
+{nbsp} +
+
+
+== download a network file
+
+//ST: download a network file
+//ST: !
+
+link:../resources/miserables_result.zip[download this zip file] and unzip it on your computer.
+
+You should find the file `miserables.gexf` in it.
+
+Save it in a folder you will remember (or create a folder specially for this small project).
+
+== description of the file / the network
+
+//ST: description of the file / the network
+//ST: !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+
+== open the network in Gephi
+//open the network in Gephi
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables.gexf` on your computer and open it
+
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+{nbsp} +
+
+== read the report after opening a file
+
+//ST: !
+A report window will open, giving you basic info on the network you opened:
+
+image::en/opening-file-report-en.png[align="center", title="report window"]
+{nbsp} +
+
+//ST: !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+== initial view
+
+//ST: !
+
+image::en/project-initial-view.png[align="center", title="initial view when opening a graph"]
+{nbsp} +
+
+This is how the network appears in Gephi. Not very useful! Let's examine what we have here.
+
+== basic view of Gephi's interface
+
+//ST: !
+
+image::the-3-main-screens-in-Gephi.png[align="center", title="the 3 main screens in Gephi"]
+{nbsp} +
+
+//ST: !
+Gephi has 3 main screens:
+
+1. Overview: where we can explore the graph visually
+2. Data Laboratory: provides an "Excel" table view of the data in network
+3. Preview: where we polish the visualization before exporting it as a pictue or pdf
+
+What we see here is the Overview.
+
+//ST: !
+
+image::Filters-and-statistics-panels-in-Gephi.png[align="center", title="Filters and statistics panels in Gephi"]
+{nbsp} +
+
+//ST: !
+
+In the Overview, the graph is shown at the center. Around it, several panels help us fine tune the visualization.
+
+[options="compact"]
+[start=4]
+4. "Filters", where we can hide different parts of the network under a variety of conditions
+5. "Statistics", where we can compute metrics on the network
+
+//ST: !
+image::Appearance-and-layout-panels-in-Gephi.png[align="center", title="Appearance and layout panels in Gephi"]
+{nbsp} +
+
+
+//ST: !
+
+[options="compact"]
+[start=6]
+6. "Appearance", where we can change colors and sizes in interesting ways
+7. "Layouts", where we can apply automated procedures to change the position of the network
+
+//ST: !
+image::3-groups-of-icons.png[align="center", title="3 groups of icons"]
+{nbsp} +
+
+//ST: !
+
+[options="compact"]
+[start=8]
+8. A series of icons to add / colorize nodes and links manually, by clicking on them
+9. Options and sliders to change the size of all nodes, links, or labels
+10. More options become visible if we click on this *little arrow head pointing up*
+
+
+== showing labels of the nodes
+
+//ST: showing labels of the nodes
+
+//ST: !
+
+image::showing-node-labels.png[align="center", title="showing node labels"]
+{nbsp} +
+
+
+== layout ("spatialize") the network
+
+//ST: layout ("spatialize") the network
+
+//ST: !
+
+image::selecting-the-force-atlas-2-layout.png[align="center", title="selecting the force atlas 2 layout"]
+{nbsp} +
+
+//ST: !
+
+[[force-atlas-2-parameters]]
+image::changing-a-few-parameters-and-launching-the-layout.png[align="center", title="changing a few parameters and launching the layout"]
+{nbsp} +
+
+
+//ST: !
+
+image::result-of-Force-Atlas-2-layout.png[align="center", title="result of Force Atlas 2 layout"]
+{nbsp} +
+
+== visualize the properties of the nodes
+
+//ST: visualize the properties of the nodes
+
+//ST: !
+
+A network consists in entities and their relations.
+This is what we just visualized.
+Yet, the properties of these entities remain invisible.
+
+For instance: the characters in the novel "Les Misérables" are male or female. Are males more likely to be connected to males, or females? Just looking at the network in Gephi, we can't tell.
+
+Now, we will see how to make this property ("Gender") visible.
+
+//ST: !
+
+image::Switching-the-view-to-the-data-laboratory.png[align="center",title="Switching the view to the data laboratory"]
+{nbsp} +
+
+//ST: !
+
+image::We-see-there-is-a-Gender-attribute-for-each-character..png[align="center",title="We see there is a Gender attribute for each character."]
+{nbsp} +
+
+//ST: !
+
+We will color the nodes based on their gender. To do that, we select `Gender` in the `Appearance` panel:
+
+image::Coloring-nodes-according-to-their-gender.png[align="center",title="Coloring nodes according to their gender"]
+{nbsp} +
+
+//ST: !
+
+The result:
+
+image::appearance-miserables-result.png[align="center",title="After coloring characters according to their gender"]
+{nbsp} +
+
+== prettify the network for enhanced readability
+
+//ST: prettify the network for enhanced readability
+//ST: !
+
+There are a number of issues with the result we get:
+
+1. the network is too big or too small, it is hard to read
+2. the labels of the characters overlap
+3. the size of the labels might be too big / small
+4. the links are sometimes too large
+
+Let's fix these issues.
+
+//ST: !
+==== 1. Enlarge or shrink the network
+
+- either we use the "scaling" parameter of the layout, as we have seen <>.
+- or the scale is fine, it is just that we need to zoom it or out. Use the scrolling wheel of your mouse, and right click to move the network.
+
+//ST: !
+==== 2. Prevent the Labels from overlapping
+
+In the layout panel, choose "Label Adjust" or "Noverlap": these layouts will move the nodes just so that the Labels stop overlapping:
+
+image::en/choosing-a-label-adjust-algo-en.png[align="center",title="Noverlap or Label Adjust will help you"]
+{nbsp} +
+
+Don't forget to click on "Run" to apply these layouts.
+
+//ST: !
+==== 3. Changing the size of the labels
+Open the bottom panel of Gephi by clicking on tiny arrow head (1). Then select "nodes" (2), then move the slider (3).
+
+//ST: !
+image::Adjusting-label-size.png[align="center",title="Adjusting label size"]
+{nbsp} +
+
+//ST: !
+==== 4. Adjusting the thickness of the links
+
+image::Adjusting-edge-thickness.png[align="center",title="Adjusting edge thickness"]
+{nbsp} +
+
+== computing the centrality of the nodes
+
+//ST: Computing the centrality of the nodes
+//ST: !
+==== 1. Definitions of centrality
+
+"Centrality" is a very good metrics to first get an idea of a network.
+What does centrality mean? Intuitively, we understand that a "central" node will probably sit in the middle of the network.
+But how to measure that "scientifically", so that we have an objective confirmation of our visual impression?
+
+There are several ways, all equally interesting.
+
+//ST: !
+We can measure `degree centrality`. "Degree" is the technical term for "number of connections that a node has".
+
+So, `degree centrality` just means that the most central node is the node which has the most connections. Simple!
+
+//ST: !
+Another measure is `betweenness centrality`. This one is more tricky.
+
+- First, you have to imagine what is a `shortest path`.
+ - A `path` from node A to node B is a chain of nodes, the road if you will, that you have to traverse to go from A to B.
+ - The `shortest path` from A to B is the quickest road from A to B: the path that has the smallest number of nodes between A and B.
+
+- A node which is on many shortest paths is "between" many nodes. And when you realize it, it is a very intuitive sense of what it means to "be central". These nodes have a high `betweenness centrality`.
+
+//ST: !
+==== 2. Computing betweenness centrality with Gephi
+
+Gephi computes it for you. Find "Network diameter" in the statistics panel and click "run":
+
+image::Computing-betweenness-centrality.png[align="center",title="Computing betweenness centrality"]
+{nbsp} +
+
+//ST: !
+This will open a window with parameters (explained in a more advanced tutorials). Click "OK":
+
+image::Parameters-for-the-computation-of-betweenness-centrality.png[align="center",title="Parameters for the computation of betweenness centrality"]
+{nbsp} +
+
+
+//ST: !
+A report window opens (also explained in a other tutorials). Close it.
+
+image::Report-after-the-computation-of-betweenness-centrality.png[align="center",title="Report after the computation of betweenness centrality"]
+{nbsp} +
+
+Now we can visualize this information.
+
+== visualize attributes created by Gephi
+
+//ST: visualize attributes created by Gephi
+Gephi has computed for us the betweenness centrality of all nodes. This remains invisible on the network, however.
+
+It would be interesting to, say, resize the nodes according to their centrality: the more central a node, the bigger.
+This would allow for a very quick visual appreciation of which nodes are the most central.
+
+//ST: !
+First, let's switch to the data laboratory to see how Gephi stored the "betweenness centrality" of each node:
+
+image::Switching-the-view-to-the-data-laboratory.png[align="center",title="Switching the view to the data laboratory"]
+{nbsp} +
+
+//ST: !
+
+When we ran "Network Diameter" in the statistics panel, Gephi has actually computed many kinds of centralities (not just "betweenness centrality"):
+
+image::Different-centrality-measures-visible-in-the-data-laboratory.png[align="center",title="Different centrality measures visible in the data laboratory"]
+{nbsp} +
+
+//ST: !
+To resize the nodes according to the value of their betweenness centrality, we use the `Appearance` panel:
+
+CAUTION: make sure you select the correct options
+
+image::Ranking-node-sizes-by-centrality.png[align="center",title="Ranking node sizes by centrality"]
+{nbsp} +
+
+//ST: !
+
+image::Selecting-the-minimum-and-maximum-sizes-of-nodes.png[align="center",title="Selecting the minimum and maximum sizes of nodes"]
+{nbsp} +
+
+//ST: !
+
+image::ranking-centrality-miserables-3--en.png[align="center",title="Result of the ranking"]
+{nbsp} +
+
+//ST: !
+
+image::Resizing-labels-to-reflect-their-node's-size.png[align="center",title="Resizing labels to reflect their node's size"]
+{nbsp} +
+
+//ST: !
+image::result-label-resizing-en.png[align="center",title="Result of the label resizing"]
+{nbsp} +
+
+== exporting a network as a picture
+
+//ST: exporting a network as a picture
+
+//ST: 1. exporting a screenshot from the Overview (a png image)
+
+//ST: !
+
+image::Open-the-configuration-panel-for-screenshots.png[align="center",title="Open the configuration panel for screenshots"]
+{nbsp} +
+
+//ST: !
+
+Select the maximum value for anti-aliasing, and multiply values for width and height for higher resolution. For example, resolution x 3 is width = 3072 and height = 2304
+
+image::en/configuration-screenshot-en.png[align="center",title="The configuration panel for screenshots"]
+{nbsp} +
+
+//ST: 2. exporting a pdf or svg picture
+
+//ST: !
+Let's switch to the preview panel:
+
+image::Switching-to-the-preview-panel.png[align="center",title="Switching to the preview panel"]
+{nbsp} +
+
+//ST: !
+The preview panel is dedicated to the preparation of the picture to be exported as a pdf or svg, which are "scalable": in pdf or sv, the resolution of the graph will remain perfect, even with a strong zoom.
+But as you see, it means the network is now looking different than in the Overview.
+
+//ST: !
+CAUTION: contrary to the Overview panel, here you need to hit the "refresh" button after each parameter change.
+
+image::Updating-the-parameters.png[align="center",title="Updating the parameters"]
+{nbsp} +
+
+//ST: !
+Here I change just 2 parameters: `Show Labels` and `Font` (which I reduce to size "5"), to get:
+
+image::Result-of-preview.png[align="center",title="Result of preview"]
+{nbsp} +
+
+//ST: !
+Export: just click on the button and select the file format you prefer
+
+image::Export-button.png[align="center",title="Export button"]
+{nbsp} +
+
+//ST: donwload the result file
+
+link:../resources/miserables_result.zip[download this zip file] if you need to see the network in its final form.
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+
+
+== questions and exercises
+
+//ST: questions and exercises
+
+//ST: !
+1. Open the file `miserables.gexf` with a text editor (here is how to do it on a http://www.dummies.com/computers/macs/how-to-open-and-edit-a-text-file-on-a-mac/[Mac], and on https://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/app_notepad.mspx?mfr=true[Windows]). See how the nodes and the links are written in the file. Can you find the character Javert?
+
+//ST: !
+[start=2]
+2. Our network of Les Miserables characters was undirected. Can you think of networks which are directed?
+
+Imagine how undirected and directed networks differ when computing centrality, for example.
+
+//ST: !
+[start=3]
+3. Force Atlas 2 is a layout which brings together connected nodes, and spreads out unconnected nodes. We might have nodes with no relation at all with other nodes (called "isolated nodes").
+
+How will these isolated nodes move on screen?
+
+//ST: !
+[start=4]
+4. When applying the Force Atlas 2 layout, the network moves quickly, then stabilizes, and then keeps moving a bit.
+
+Can you guess why this is happening?
+
+//ST: !
+[start=5]
+5. In the list of layouts, Force Atlas 2 is just one of many options you can choose.
+Try "Fruchterman Reingold" and "Yfan Hu".
+
+These are layouts which follow the same logic as Force Atlas 2, but with slight variations. Explore how these algorithms result in similar, yet specific layouts.
+
+//ST: !
+[start=6]
+6. In this tutorial, we defined degree centrality.
+Can you imagine a situation when a node with the largest degree centrality will actually be in the periphery of the network? You can draw a toy network to help you figure.
diff --git a/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_html.md b/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_html.md
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+= Simple Gephi Project from A to Z
+Clément Levallois
+2017-01-20
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+
+== Description of the project
+
+//ST: Description of the project
+//ST: !
+
+
+This project is for complete beginners to Gephi.
+It supposes you have Gephi installed and running on your computer. That is all.
+
+When finishing this tutorial, you should be able to:
+[options="compact"]
+- be familiar with the vocabulary to discuss networks
+- download a network file for this exercise
+- description of the file / the network
+//ST: !
+- open a network file
+- read the report after opening a file
+- show the labels of the nodes
+- layout the network
+//ST: !
+- visualize attributes of the network
+- prettify the network for enhanced readability
+- compute the centrality of the nodes in the network
+- visualize attributes created by Gephi
+- export a visualization as a picture or pdf
+
+
+== be familiar with the terminology to discuss networks
+//ST: terminology to discuss networks
+//ST: !
+image::terminology-for-networks.png[align="center",title="terminology for networks"]
+{nbsp} +
+
+
+== download a network file
+
+//ST: download a network file
+//ST: !
+
+link:../resources/miserables_result.zip[download this zip file] and unzip it on your computer.
+
+You should find the file `miserables.gexf` in it.
+
+Save it in a folder you will remember (or create a folder specially for this small project).
+
+== description of the file / the network
+
+//ST: description of the file / the network
+//ST: !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+
+== open the network in Gephi
+//open the network in Gephi
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables.gexf` on your computer and open it
+
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+{nbsp} +
+
+== read the report after opening a file
+
+//ST: !
+A report window will open, giving you basic info on the network you opened:
+
+image::en/opening-file-report-en.png[align="center", title="report window"]
+{nbsp} +
+
+//ST: !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+== initial view
+
+//ST: !
+
+image::en/project-initial-view.png[align="center", title="initial view when opening a graph"]
+{nbsp} +
+
+This is how the network appears in Gephi. Not very useful! Let's examine what we have here.
+
+== basic view of Gephi's interface
+
+//ST: !
+
+image::the-3-main-screens-in-Gephi.png[align="center", title="the 3 main screens in Gephi"]
+{nbsp} +
+
+//ST: !
+Gephi has 3 main screens:
+
+1. Overview: where we can explore the graph visually
+2. Data Laboratory: provides an "Excel" table view of the data in network
+3. Preview: where we polish the visualization before exporting it as a pictue or pdf
+
+What we see here is the Overview.
+
+//ST: !
+
+image::Filters-and-statistics-panels-in-Gephi.png[align="center", title="Filters and statistics panels in Gephi"]
+{nbsp} +
+
+//ST: !
+
+In the Overview, the graph is shown at the center. Around it, several panels help us fine tune the visualization.
+
+[options="compact"]
+[start=4]
+4. "Filters", where we can hide different parts of the network under a variety of conditions
+5. "Statistics", where we can compute metrics on the network
+
+//ST: !
+image::Appearance-and-layout-panels-in-Gephi.png[align="center", title="Appearance and layout panels in Gephi"]
+{nbsp} +
+
+
+//ST: !
+
+[options="compact"]
+[start=6]
+6. "Appearance", where we can change colors and sizes in interesting ways
+7. "Layouts", where we can apply automated procedures to change the position of the network
+
+//ST: !
+image::3-groups-of-icons.png[align="center", title="3 groups of icons"]
+{nbsp} +
+
+//ST: !
+
+[options="compact"]
+[start=8]
+8. A series of icons to add / colorize nodes and links manually, by clicking on them
+9. Options and sliders to change the size of all nodes, links, or labels
+10. More options become visible if we click on this *little arrow head pointing up*
+
+
+== showing labels of the nodes
+
+//ST: showing labels of the nodes
+
+//ST: !
+
+image::showing-node-labels.png[align="center", title="showing node labels"]
+{nbsp} +
+
+
+== layout ("spatialize") the network
+
+//ST: layout ("spatialize") the network
+
+//ST: !
+
+image::selecting-the-force-atlas-2-layout.png[align="center", title="selecting the force atlas 2 layout"]
+{nbsp} +
+
+//ST: !
+
+[[force-atlas-2-parameters]]
+image::changing-a-few-parameters-and-launching-the-layout.png[align="center", title="changing a few parameters and launching the layout"]
+{nbsp} +
+
+
+//ST: !
+
+image::result-of-Force-Atlas-2-layout.png[align="center", title="result of Force Atlas 2 layout"]
+{nbsp} +
+
+== visualize the properties of the nodes
+
+//ST: visualize the properties of the nodes
+
+//ST: !
+
+A network consists in entities and their relations.
+This is what we just visualized.
+Yet, the properties of these entities remain invisible.
+
+For instance: the characters in the novel "Les Misérables" are male or female. Are males more likely to be connected to males, or females? Just looking at the network in Gephi, we can't tell.
+
+Now, we will see how to make this property ("Gender") visible.
+
+//ST: !
+
+image::Switching-the-view-to-the-data-laboratory.png[align="center",title="Switching the view to the data laboratory"]
+{nbsp} +
+
+//ST: !
+
+image::We-see-there-is-a-Gender-attribute-for-each-character..png[align="center",title="We see there is a Gender attribute for each character."]
+{nbsp} +
+
+//ST: !
+
+We will color the nodes based on their gender. To do that, we select `Gender` in the `Appearance` panel:
+
+image::Coloring-nodes-according-to-their-gender.png[align="center",title="Coloring nodes according to their gender"]
+{nbsp} +
+
+//ST: !
+
+The result:
+
+image::appearance-miserables-result.png[align="center",title="After coloring characters according to their gender"]
+{nbsp} +
+
+== prettify the network for enhanced readability
+
+//ST: prettify the network for enhanced readability
+//ST: !
+
+There are a number of issues with the result we get:
+
+1. the network is too big or too small, it is hard to read
+2. the labels of the characters overlap
+3. the size of the labels might be too big / small
+4. the links are sometimes too large
+
+Let's fix these issues.
+
+//ST: !
+==== 1. Enlarge or shrink the network
+
+- either we use the "scaling" parameter of the layout, as we have seen <>.
+- or the scale is fine, it is just that we need to zoom it or out. Use the scrolling wheel of your mouse, and right click to move the network.
+
+//ST: !
+==== 2. Prevent the Labels from overlapping
+
+In the layout panel, choose "Label Adjust" or "Noverlap": these layouts will move the nodes just so that the Labels stop overlapping:
+
+image::en/choosing-a-label-adjust-algo-en.png[align="center",title="Noverlap or Label Adjust will help you"]
+{nbsp} +
+
+Don't forget to click on "Run" to apply these layouts.
+
+//ST: !
+==== 3. Changing the size of the labels
+Open the bottom panel of Gephi by clicking on tiny arrow head (1). Then select "nodes" (2), then move the slider (3).
+
+//ST: !
+image::Adjusting-label-size.png[align="center",title="Adjusting label size"]
+{nbsp} +
+
+//ST: !
+==== 4. Adjusting the thickness of the links
+
+image::Adjusting-edge-thickness.png[align="center",title="Adjusting edge thickness"]
+{nbsp} +
+
+== computing the centrality of the nodes
+
+//ST: Computing the centrality of the nodes
+//ST: !
+==== 1. Definitions of centrality
+
+"Centrality" is a very good metrics to first get an idea of a network.
+What does centrality mean? Intuitively, we understand that a "central" node will probably sit in the middle of the network.
+But how to measure that "scientifically", so that we have an objective confirmation of our visual impression?
+
+There are several ways, all equally interesting.
+
+//ST: !
+We can measure `degree centrality`. "Degree" is the technical term for "number of connections that a node has".
+
+So, `degree centrality` just means that the most central node is the node which has the most connections. Simple!
+
+//ST: !
+Another measure is `betweenness centrality`. This one is more tricky.
+
+- First, you have to imagine what is a `shortest path`.
+ - A `path` from node A to node B is a chain of nodes, the road if you will, that you have to traverse to go from A to B.
+ - The `shortest path` from A to B is the quickest road from A to B: the path that has the smallest number of nodes between A and B.
+
+- A node which is on many shortest paths is "between" many nodes. And when you realize it, it is a very intuitive sense of what it means to "be central". These nodes have a high `betweenness centrality`.
+
+//ST: !
+==== 2. Computing betweenness centrality with Gephi
+
+Gephi computes it for you. Find "Network diameter" in the statistics panel and click "run":
+
+image::Computing-betweenness-centrality.png[align="center",title="Computing betweenness centrality"]
+{nbsp} +
+
+//ST: !
+This will open a window with parameters (explained in a more advanced tutorials). Click "OK":
+
+image::Parameters-for-the-computation-of-betweenness-centrality.png[align="center",title="Parameters for the computation of betweenness centrality"]
+{nbsp} +
+
+
+//ST: !
+A report window opens (also explained in a other tutorials). Close it.
+
+image::Report-after-the-computation-of-betweenness-centrality.png[align="center",title="Report after the computation of betweenness centrality"]
+{nbsp} +
+
+Now we can visualize this information.
+
+== visualize attributes created by Gephi
+
+//ST: visualize attributes created by Gephi
+Gephi has computed for us the betweenness centrality of all nodes. This remains invisible on the network, however.
+
+It would be interesting to, say, resize the nodes according to their centrality: the more central a node, the bigger.
+This would allow for a very quick visual appreciation of which nodes are the most central.
+
+//ST: !
+First, let's switch to the data laboratory to see how Gephi stored the "betweenness centrality" of each node:
+
+image::Switching-the-view-to-the-data-laboratory.png[align="center",title="Switching the view to the data laboratory"]
+{nbsp} +
+
+//ST: !
+
+When we ran "Network Diameter" in the statistics panel, Gephi has actually computed many kinds of centralities (not just "betweenness centrality"):
+
+image::Different-centrality-measures-visible-in-the-data-laboratory.png[align="center",title="Different centrality measures visible in the data laboratory"]
+{nbsp} +
+
+//ST: !
+To resize the nodes according to the value of their betweenness centrality, we use the `Appearance` panel:
+
+CAUTION: make sure you select the correct options
+
+image::Ranking-node-sizes-by-centrality.png[align="center",title="Ranking node sizes by centrality"]
+{nbsp} +
+
+//ST: !
+
+image::Selecting-the-minimum-and-maximum-sizes-of-nodes.png[align="center",title="Selecting the minimum and maximum sizes of nodes"]
+{nbsp} +
+
+//ST: !
+
+image::ranking-centrality-miserables-3--en.png[align="center",title="Result of the ranking"]
+{nbsp} +
+
+//ST: !
+
+image::Resizing-labels-to-reflect-their-node's-size.png[align="center",title="Resizing labels to reflect their node's size"]
+{nbsp} +
+
+//ST: !
+image::result-label-resizing-en.png[align="center",title="Result of the label resizing"]
+{nbsp} +
+
+== exporting a network as a picture
+
+//ST: exporting a network as a picture
+
+//ST: 1. exporting a screenshot from the Overview (a png image)
+
+//ST: !
+
+image::Open-the-configuration-panel-for-screenshots.png[align="center",title="Open the configuration panel for screenshots"]
+{nbsp} +
+
+//ST: !
+
+Select the maximum value for anti-aliasing, and multiply values for width and height for higher resolution. For example, resolution x 3 is width = 3072 and height = 2304
+
+image::en/configuration-screenshot-en.png[align="center",title="The configuration panel for screenshots"]
+{nbsp} +
+
+//ST: 2. exporting a pdf or svg picture
+
+//ST: !
+Let's switch to the preview panel:
+
+image::Switching-to-the-preview-panel.png[align="center",title="Switching to the preview panel"]
+{nbsp} +
+
+//ST: !
+The preview panel is dedicated to the preparation of the picture to be exported as a pdf or svg, which are "scalable": in pdf or sv, the resolution of the graph will remain perfect, even with a strong zoom.
+But as you see, it means the network is now looking different than in the Overview.
+
+//ST: !
+CAUTION: contrary to the Overview panel, here you need to hit the "refresh" button after each parameter change.
+
+image::Updating-the-parameters.png[align="center",title="Updating the parameters"]
+{nbsp} +
+
+//ST: !
+Here I change just 2 parameters: `Show Labels` and `Font` (which I reduce to size "5"), to get:
+
+image::Result-of-preview.png[align="center",title="Result of preview"]
+{nbsp} +
+
+//ST: !
+Export: just click on the button and select the file format you prefer
+
+image::Export-button.png[align="center",title="Export button"]
+{nbsp} +
+
+//ST: donwload the result file
+
+link:../resources/miserables_result.zip[download this zip file] if you need to see the network in its final form.
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+
+
+== questions and exercises
+
+//ST: questions and exercises
+
+//ST: !
+1. Open the file `miserables.gexf` with a text editor (here is how to do it on a http://www.dummies.com/computers/macs/how-to-open-and-edit-a-text-file-on-a-mac/[Mac], and on https://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/app_notepad.mspx?mfr=true[Windows]). See how the nodes and the links are written in the file. Can you find the character Javert?
+
+//ST: !
+[start=2]
+2. Our network of Les Miserables characters was undirected. Can you think of networks which are directed?
+
+Imagine how undirected and directed networks differ when computing centrality, for example.
+
+//ST: !
+[start=3]
+3. Force Atlas 2 is a layout which brings together connected nodes, and spreads out unconnected nodes. We might have nodes with no relation at all with other nodes (called "isolated nodes").
+
+How will these isolated nodes move on screen?
+
+//ST: !
+[start=4]
+4. When applying the Force Atlas 2 layout, the network moves quickly, then stabilizes, and then keeps moving a bit.
+
+Can you guess why this is happening?
+
+//ST: !
+[start=5]
+5. In the list of layouts, Force Atlas 2 is just one of many options you can choose.
+Try "Fruchterman Reingold" and "Yfan Hu".
+
+These are layouts which follow the same logic as Force Atlas 2, but with slight variations. Explore how these algorithms result in similar, yet specific layouts.
+
+//ST: !
+[start=6]
+6. In this tutorial, we defined degree centrality.
+Can you imagine a situation when a node with the largest degree centrality will actually be in the periphery of the network? You can draw a toy network to help you figure.
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_pdf.md b/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_pdf.md
new file mode 100644
index 0000000..c8aae23
--- /dev/null
+++ b/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_pdf.md
@@ -0,0 +1,474 @@
+= Simple Gephi Project from A to Z
+Clément Levallois
+2017-01-20
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+
+== Description of the project
+
+//ST: Description of the project
+//ST: !
+
+
+This project is for complete beginners to Gephi.
+It supposes you have Gephi installed and running on your computer. That is all.
+
+When finishing this tutorial, you should be able to:
+[options="compact"]
+- be familiar with the vocabulary to discuss networks
+- download a network file for this exercise
+- description of the file / the network
+//ST: !
+- open a network file
+- read the report after opening a file
+- show the labels of the nodes
+- layout the network
+//ST: !
+- visualize attributes of the network
+- prettify the network for enhanced readability
+- compute the centrality of the nodes in the network
+- visualize attributes created by Gephi
+- export a visualization as a picture or pdf
+
+
+== be familiar with the terminology to discuss networks
+//ST: terminology to discuss networks
+//ST: !
+image::terminology-for-networks.png[align="center",title="terminology for networks"]
+{nbsp} +
+
+
+== download a network file
+
+//ST: download a network file
+//ST: !
+
+link:../resources/miserables_result.zip[download this zip file] and unzip it on your computer.
+
+You should find the file `miserables.gexf` in it.
+
+Save it in a folder you will remember (or create a folder specially for this small project).
+
+== description of the file / the network
+
+//ST: description of the file / the network
+//ST: !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+
+== open the network in Gephi
+//open the network in Gephi
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables.gexf` on your computer and open it
+
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+{nbsp} +
+
+== read the report after opening a file
+
+//ST: !
+A report window will open, giving you basic info on the network you opened:
+
+image::en/opening-file-report-en.png[align="center", title="report window"]
+{nbsp} +
+
+//ST: !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+== initial view
+
+//ST: !
+
+image::en/project-initial-view.png[align="center", title="initial view when opening a graph"]
+{nbsp} +
+
+This is how the network appears in Gephi. Not very useful! Let's examine what we have here.
+
+== basic view of Gephi's interface
+
+//ST: !
+
+image::the-3-main-screens-in-Gephi.png[align="center", title="the 3 main screens in Gephi"]
+{nbsp} +
+
+//ST: !
+Gephi has 3 main screens:
+
+1. Overview: where we can explore the graph visually
+2. Data Laboratory: provides an "Excel" table view of the data in network
+3. Preview: where we polish the visualization before exporting it as a pictue or pdf
+
+What we see here is the Overview.
+
+//ST: !
+
+image::Filters-and-statistics-panels-in-Gephi.png[align="center", title="Filters and statistics panels in Gephi"]
+{nbsp} +
+
+//ST: !
+
+In the Overview, the graph is shown at the center. Around it, several panels help us fine tune the visualization.
+
+[options="compact"]
+[start=4]
+4. "Filters", where we can hide different parts of the network under a variety of conditions
+5. "Statistics", where we can compute metrics on the network
+
+//ST: !
+image::Appearance-and-layout-panels-in-Gephi.png[align="center", title="Appearance and layout panels in Gephi"]
+{nbsp} +
+
+
+//ST: !
+
+[options="compact"]
+[start=6]
+6. "Appearance", where we can change colors and sizes in interesting ways
+7. "Layouts", where we can apply automated procedures to change the position of the network
+
+//ST: !
+image::3-groups-of-icons.png[align="center", title="3 groups of icons"]
+{nbsp} +
+
+//ST: !
+
+[options="compact"]
+[start=8]
+8. A series of icons to add / colorize nodes and links manually, by clicking on them
+9. Options and sliders to change the size of all nodes, links, or labels
+10. More options become visible if we click on this *little arrow head pointing up*
+
+
+== showing labels of the nodes
+
+//ST: showing labels of the nodes
+
+//ST: !
+
+image::showing-node-labels.png[align="center", title="showing node labels"]
+{nbsp} +
+
+
+== layout ("spatialize") the network
+
+//ST: layout ("spatialize") the network
+
+//ST: !
+
+image::selecting-the-force-atlas-2-layout.png[align="center", title="selecting the force atlas 2 layout"]
+{nbsp} +
+
+//ST: !
+
+[[force-atlas-2-parameters]]
+image::changing-a-few-parameters-and-launching-the-layout.png[align="center", title="changing a few parameters and launching the layout"]
+{nbsp} +
+
+
+//ST: !
+
+image::result-of-Force-Atlas-2-layout.png[align="center", title="result of Force Atlas 2 layout"]
+{nbsp} +
+
+== visualize the properties of the nodes
+
+//ST: visualize the properties of the nodes
+
+//ST: !
+
+A network consists in entities and their relations.
+This is what we just visualized.
+Yet, the properties of these entities remain invisible.
+
+For instance: the characters in the novel "Les Misérables" are male or female. Are males more likely to be connected to males, or females? Just looking at the network in Gephi, we can't tell.
+
+Now, we will see how to make this property ("Gender") visible.
+
+//ST: !
+
+image::Switching-the-view-to-the-data-laboratory.png[align="center",title="Switching the view to the data laboratory"]
+{nbsp} +
+
+//ST: !
+
+image::We-see-there-is-a-Gender-attribute-for-each-character..png[align="center",title="We see there is a Gender attribute for each character."]
+{nbsp} +
+
+//ST: !
+
+We will color the nodes based on their gender. To do that, we select `Gender` in the `Appearance` panel:
+
+image::Coloring-nodes-according-to-their-gender.png[align="center",title="Coloring nodes according to their gender"]
+{nbsp} +
+
+//ST: !
+
+The result:
+
+image::appearance-miserables-result.png[align="center",title="After coloring characters according to their gender"]
+{nbsp} +
+
+== prettify the network for enhanced readability
+
+//ST: prettify the network for enhanced readability
+//ST: !
+
+There are a number of issues with the result we get:
+
+1. the network is too big or too small, it is hard to read
+2. the labels of the characters overlap
+3. the size of the labels might be too big / small
+4. the links are sometimes too large
+
+Let's fix these issues.
+
+//ST: !
+==== 1. Enlarge or shrink the network
+
+- either we use the "scaling" parameter of the layout, as we have seen <>.
+- or the scale is fine, it is just that we need to zoom it or out. Use the scrolling wheel of your mouse, and right click to move the network.
+
+//ST: !
+==== 2. Prevent the Labels from overlapping
+
+In the layout panel, choose "Label Adjust" or "Noverlap": these layouts will move the nodes just so that the Labels stop overlapping:
+
+image::en/choosing-a-label-adjust-algo-en.png[align="center",title="Noverlap or Label Adjust will help you"]
+{nbsp} +
+
+Don't forget to click on "Run" to apply these layouts.
+
+//ST: !
+==== 3. Changing the size of the labels
+Open the bottom panel of Gephi by clicking on tiny arrow head (1). Then select "nodes" (2), then move the slider (3).
+
+//ST: !
+image::Adjusting-label-size.png[align="center",title="Adjusting label size"]
+{nbsp} +
+
+//ST: !
+==== 4. Adjusting the thickness of the links
+
+image::Adjusting-edge-thickness.png[align="center",title="Adjusting edge thickness"]
+{nbsp} +
+
+== computing the centrality of the nodes
+
+//ST: Computing the centrality of the nodes
+//ST: !
+==== 1. Definitions of centrality
+
+"Centrality" is a very good metrics to first get an idea of a network.
+What does centrality mean? Intuitively, we understand that a "central" node will probably sit in the middle of the network.
+But how to measure that "scientifically", so that we have an objective confirmation of our visual impression?
+
+There are several ways, all equally interesting.
+
+//ST: !
+We can measure `degree centrality`. "Degree" is the technical term for "number of connections that a node has".
+
+So, `degree centrality` just means that the most central node is the node which has the most connections. Simple!
+
+//ST: !
+Another measure is `betweenness centrality`. This one is more tricky.
+
+- First, you have to imagine what is a `shortest path`.
+ - A `path` from node A to node B is a chain of nodes, the road if you will, that you have to traverse to go from A to B.
+ - The `shortest path` from A to B is the quickest road from A to B: the path that has the smallest number of nodes between A and B.
+
+- A node which is on many shortest paths is "between" many nodes. And when you realize it, it is a very intuitive sense of what it means to "be central". These nodes have a high `betweenness centrality`.
+
+//ST: !
+==== 2. Computing betweenness centrality with Gephi
+
+Gephi computes it for you. Find "Network diameter" in the statistics panel and click "run":
+
+image::Computing-betweenness-centrality.png[align="center",title="Computing betweenness centrality"]
+{nbsp} +
+
+//ST: !
+This will open a window with parameters (explained in a more advanced tutorials). Click "OK":
+
+image::Parameters-for-the-computation-of-betweenness-centrality.png[align="center",title="Parameters for the computation of betweenness centrality"]
+{nbsp} +
+
+
+//ST: !
+A report window opens (also explained in a other tutorials). Close it.
+
+image::Report-after-the-computation-of-betweenness-centrality.png[align="center",title="Report after the computation of betweenness centrality"]
+{nbsp} +
+
+Now we can visualize this information.
+
+== visualize attributes created by Gephi
+
+//ST: visualize attributes created by Gephi
+Gephi has computed for us the betweenness centrality of all nodes. This remains invisible on the network, however.
+
+It would be interesting to, say, resize the nodes according to their centrality: the more central a node, the bigger.
+This would allow for a very quick visual appreciation of which nodes are the most central.
+
+//ST: !
+First, let's switch to the data laboratory to see how Gephi stored the "betweenness centrality" of each node:
+
+image::Switching-the-view-to-the-data-laboratory.png[align="center",title="Switching the view to the data laboratory"]
+{nbsp} +
+
+//ST: !
+
+When we ran "Network Diameter" in the statistics panel, Gephi has actually computed many kinds of centralities (not just "betweenness centrality"):
+
+image::Different-centrality-measures-visible-in-the-data-laboratory.png[align="center",title="Different centrality measures visible in the data laboratory"]
+{nbsp} +
+
+//ST: !
+To resize the nodes according to the value of their betweenness centrality, we use the `Appearance` panel:
+
+CAUTION: make sure you select the correct options
+
+image::Ranking-node-sizes-by-centrality.png[align="center",title="Ranking node sizes by centrality"]
+{nbsp} +
+
+//ST: !
+
+image::Selecting-the-minimum-and-maximum-sizes-of-nodes.png[align="center",title="Selecting the minimum and maximum sizes of nodes"]
+{nbsp} +
+
+//ST: !
+
+image::ranking-centrality-miserables-3--en.png[align="center",title="Result of the ranking"]
+{nbsp} +
+
+//ST: !
+
+image::Resizing-labels-to-reflect-their-node's-size.png[align="center",title="Resizing labels to reflect their node's size"]
+{nbsp} +
+
+//ST: !
+image::result-label-resizing-en.png[align="center",title="Result of the label resizing"]
+{nbsp} +
+
+== exporting a network as a picture
+
+//ST: exporting a network as a picture
+
+//ST: 1. exporting a screenshot from the Overview (a png image)
+
+//ST: !
+
+image::Open-the-configuration-panel-for-screenshots.png[align="center",title="Open the configuration panel for screenshots"]
+{nbsp} +
+
+//ST: !
+
+Select the maximum value for anti-aliasing, and multiply values for width and height for higher resolution. For example, resolution x 3 is width = 3072 and height = 2304
+
+image::en/configuration-screenshot-en.png[align="center",title="The configuration panel for screenshots"]
+{nbsp} +
+
+//ST: 2. exporting a pdf or svg picture
+
+//ST: !
+Let's switch to the preview panel:
+
+image::Switching-to-the-preview-panel.png[align="center",title="Switching to the preview panel"]
+{nbsp} +
+
+//ST: !
+The preview panel is dedicated to the preparation of the picture to be exported as a pdf or svg, which are "scalable": in pdf or sv, the resolution of the graph will remain perfect, even with a strong zoom.
+But as you see, it means the network is now looking different than in the Overview.
+
+//ST: !
+CAUTION: contrary to the Overview panel, here you need to hit the "refresh" button after each parameter change.
+
+image::Updating-the-parameters.png[align="center",title="Updating the parameters"]
+{nbsp} +
+
+//ST: !
+Here I change just 2 parameters: `Show Labels` and `Font` (which I reduce to size "5"), to get:
+
+image::Result-of-preview.png[align="center",title="Result of preview"]
+{nbsp} +
+
+//ST: !
+Export: just click on the button and select the file format you prefer
+
+image::Export-button.png[align="center",title="Export button"]
+{nbsp} +
+
+//ST: donwload the result file
+
+link:../resources/miserables_result.zip[download this zip file] if you need to see the network in its final form.
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+
+
+== questions and exercises
+
+//ST: questions and exercises
+
+//ST: !
+1. Open the file `miserables.gexf` with a text editor (here is how to do it on a http://www.dummies.com/computers/macs/how-to-open-and-edit-a-text-file-on-a-mac/[Mac], and on https://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/app_notepad.mspx?mfr=true[Windows]). See how the nodes and the links are written in the file. Can you find the character Javert?
+
+//ST: !
+[start=2]
+2. Our network of Les Miserables characters was undirected. Can you think of networks which are directed?
+
+Imagine how undirected and directed networks differ when computing centrality, for example.
+
+//ST: !
+[start=3]
+3. Force Atlas 2 is a layout which brings together connected nodes, and spreads out unconnected nodes. We might have nodes with no relation at all with other nodes (called "isolated nodes").
+
+How will these isolated nodes move on screen?
+
+//ST: !
+[start=4]
+4. When applying the Force Atlas 2 layout, the network moves quickly, then stabilizes, and then keeps moving a bit.
+
+Can you guess why this is happening?
+
+//ST: !
+[start=5]
+5. In the list of layouts, Force Atlas 2 is just one of many options you can choose.
+Try "Fruchterman Reingold" and "Yfan Hu".
+
+These are layouts which follow the same logic as Force Atlas 2, but with slight variations. Explore how these algorithms result in similar, yet specific layouts.
+
+//ST: !
+[start=6]
+6. In this tutorial, we defined degree centrality.
+Can you imagine a situation when a node with the largest degree centrality will actually be in the periphery of the network? You can draw a toy network to help you figure.
diff --git a/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_slides.md b/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_slides.md
new file mode 100644
index 0000000..7e9e337
--- /dev/null
+++ b/docs/generated-pdf/subdir/simple-project-from-a-to-z-en_temp_slides.md
@@ -0,0 +1,509 @@
+= Simple Gephi Project from A to Z
+Clément Levallois
+2017-01-20
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+
+
+== Description of the project
+== !
+
+
+This project is for complete beginners to Gephi.
+It supposes you have Gephi installed and running on your computer. That is all.
+
+When finishing this tutorial, you should be able to:
+[options="compact"]
+- be familiar with the vocabulary to discuss networks
+- download a network file for this exercise
+- description of the file / the network
+== !
+- open a network file
+- read the report after opening a file
+- show the labels of the nodes
+- layout the network
+== !
+- visualize attributes of the network
+- prettify the network for enhanced readability
+- compute the centrality of the nodes in the network
+- visualize attributes created by Gephi
+- export a visualization as a picture or pdf
+
+
+== terminology to discuss networks
+== !
+[.stretch]
+image::terminology-for-networks.png[align="center",title="terminology for networks"]
+
+
+
+
+== download a network file
+== !
+
+link:../resources/miserables_result.zip[download this zip file] and unzip it on your computer.
+
+You should find the file `miserables.gexf` in it.
+
+Save it in a folder you will remember (or create a folder specially for this small project).
+
+
+== description of the file / the network
+== !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+
+//open the network in Gephi
+== !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables.gexf` on your computer and open it
+
+[.stretch]
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+
+
+
+== !
+A report window will open, giving you basic info on the network you opened:
+
+[.stretch]
+image::en/opening-file-report-en.png[align="center", title="report window"]
+
+
+== !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+
+== !
+
+[.stretch]
+image::en/project-initial-view.png[align="center", title="initial view when opening a graph"]
+
+
+This is how the network appears in Gephi. Not very useful! Let's examine what we have here.
+
+
+== !
+
+[.stretch]
+image::the-3-main-screens-in-Gephi.png[align="center", title="the 3 main screens in Gephi"]
+
+
+== !
+Gephi has 3 main screens:
+
+1. Overview: where we can explore the graph visually
+2. Data Laboratory: provides an "Excel" table view of the data in network
+3. Preview: where we polish the visualization before exporting it as a pictue or pdf
+
+What we see here is the Overview.
+
+== !
+
+[.stretch]
+image::Filters-and-statistics-panels-in-Gephi.png[align="center", title="Filters and statistics panels in Gephi"]
+
+
+== !
+
+In the Overview, the graph is shown at the center. Around it, several panels help us fine tune the visualization.
+
+[options="compact"]
+[start=4]
+4. "Filters", where we can hide different parts of the network under a variety of conditions
+5. "Statistics", where we can compute metrics on the network
+
+== !
+[.stretch]
+image::Appearance-and-layout-panels-in-Gephi.png[align="center", title="Appearance and layout panels in Gephi"]
+
+
+
+== !
+
+[options="compact"]
+[start=6]
+6. "Appearance", where we can change colors and sizes in interesting ways
+7. "Layouts", where we can apply automated procedures to change the position of the network
+
+== !
+[.stretch]
+image::3-groups-of-icons.png[align="center", title="3 groups of icons"]
+
+
+== !
+
+[options="compact"]
+[start=8]
+8. A series of icons to add / colorize nodes and links manually, by clicking on them
+9. Options and sliders to change the size of all nodes, links, or labels
+10. More options become visible if we click on this *little arrow head pointing up*
+
+
+
+== showing labels of the nodes
+
+== !
+
+[.stretch]
+image::showing-node-labels.png[align="center", title="showing node labels"]
+
+
+
+
+== layout ("spatialize") the network
+
+== !
+
+[.stretch]
+image::selecting-the-force-atlas-2-layout.png[align="center", title="selecting the force atlas 2 layout"]
+
+
+== !
+
+[[force-atlas-2-parameters]]
+[.stretch]
+image::changing-a-few-parameters-and-launching-the-layout.png[align="center", title="changing a few parameters and launching the layout"]
+
+
+
+== !
+
+[.stretch]
+image::result-of-Force-Atlas-2-layout.png[align="center", title="result of Force Atlas 2 layout"]
+
+
+
+== visualize the properties of the nodes
+
+== !
+
+A network consists in entities and their relations.
+This is what we just visualized.
+Yet, the properties of these entities remain invisible.
+
+For instance: the characters in the novel "Les Misérables" are male or female. Are males more likely to be connected to males, or females? Just looking at the network in Gephi, we can't tell.
+
+Now, we will see how to make this property ("Gender") visible.
+
+== !
+
+[.stretch]
+image::Switching-the-view-to-the-data-laboratory.png[align="center",title="Switching the view to the data laboratory"]
+
+
+== !
+
+[.stretch]
+image::We-see-there-is-a-Gender-attribute-for-each-character..png[align="center",title="We see there is a Gender attribute for each character."]
+
+
+== !
+
+We will color the nodes based on their gender. To do that, we select `Gender` in the `Appearance` panel:
+
+[.stretch]
+image::Coloring-nodes-according-to-their-gender.png[align="center",title="Coloring nodes according to their gender"]
+
+
+== !
+
+The result:
+
+[.stretch]
+image::appearance-miserables-result.png[align="center",title="After coloring characters according to their gender"]
+
+
+
+== prettify the network for enhanced readability
+== !
+
+There are a number of issues with the result we get:
+
+1. the network is too big or too small, it is hard to read
+2. the labels of the characters overlap
+3. the size of the labels might be too big / small
+4. the links are sometimes too large
+
+Let's fix these issues.
+
+== !
+==== 1. Enlarge or shrink the network
+
+- either we use the "scaling" parameter of the layout, as we have seen <>.
+- or the scale is fine, it is just that we need to zoom it or out. Use the scrolling wheel of your mouse, and right click to move the network.
+
+== !
+==== 2. Prevent the Labels from overlapping
+
+In the layout panel, choose "Label Adjust" or "Noverlap": these layouts will move the nodes just so that the Labels stop overlapping:
+
+[.stretch]
+image::en/choosing-a-label-adjust-algo-en.png[align="center",title="Noverlap or Label Adjust will help you"]
+
+
+Don't forget to click on "Run" to apply these layouts.
+
+== !
+==== 3. Changing the size of the labels
+Open the bottom panel of Gephi by clicking on tiny arrow head (1). Then select "nodes" (2), then move the slider (3).
+
+== !
+[.stretch]
+image::Adjusting-label-size.png[align="center",title="Adjusting label size"]
+
+
+== !
+==== 4. Adjusting the thickness of the links
+
+[.stretch]
+image::Adjusting-edge-thickness.png[align="center",title="Adjusting edge thickness"]
+
+
+
+== Computing the centrality of the nodes
+== !
+==== 1. Definitions of centrality
+
+"Centrality" is a very good metrics to first get an idea of a network.
+What does centrality mean? Intuitively, we understand that a "central" node will probably sit in the middle of the network.
+But how to measure that "scientifically", so that we have an objective confirmation of our visual impression?
+
+There are several ways, all equally interesting.
+
+== !
+We can measure `degree centrality`. "Degree" is the technical term for "number of connections that a node has".
+
+So, `degree centrality` just means that the most central node is the node which has the most connections. Simple!
+
+== !
+Another measure is `betweenness centrality`. This one is more tricky.
+
+- First, you have to imagine what is a `shortest path`.
+ - A `path` from node A to node B is a chain of nodes, the road if you will, that you have to traverse to go from A to B.
+ - The `shortest path` from A to B is the quickest road from A to B: the path that has the smallest number of nodes between A and B.
+
+- A node which is on many shortest paths is "between" many nodes. And when you realize it, it is a very intuitive sense of what it means to "be central". These nodes have a high `betweenness centrality`.
+
+== !
+==== 2. Computing betweenness centrality with Gephi
+
+Gephi computes it for you. Find "Network diameter" in the statistics panel and click "run":
+
+[.stretch]
+image::Computing-betweenness-centrality.png[align="center",title="Computing betweenness centrality"]
+
+
+== !
+This will open a window with parameters (explained in a more advanced tutorials). Click "OK":
+
+[.stretch]
+image::Parameters-for-the-computation-of-betweenness-centrality.png[align="center",title="Parameters for the computation of betweenness centrality"]
+
+
+
+== !
+A report window opens (also explained in a other tutorials). Close it.
+
+[.stretch]
+image::Report-after-the-computation-of-betweenness-centrality.png[align="center",title="Report after the computation of betweenness centrality"]
+
+
+Now we can visualize this information.
+
+
+== visualize attributes created by Gephi
+Gephi has computed for us the betweenness centrality of all nodes. This remains invisible on the network, however.
+
+It would be interesting to, say, resize the nodes according to their centrality: the more central a node, the bigger.
+This would allow for a very quick visual appreciation of which nodes are the most central.
+
+== !
+First, let's switch to the data laboratory to see how Gephi stored the "betweenness centrality" of each node:
+
+[.stretch]
+image::Switching-the-view-to-the-data-laboratory.png[align="center",title="Switching the view to the data laboratory"]
+
+
+== !
+
+When we ran "Network Diameter" in the statistics panel, Gephi has actually computed many kinds of centralities (not just "betweenness centrality"):
+
+[.stretch]
+image::Different-centrality-measures-visible-in-the-data-laboratory.png[align="center",title="Different centrality measures visible in the data laboratory"]
+
+
+== !
+To resize the nodes according to the value of their betweenness centrality, we use the `Appearance` panel:
+
+CAUTION: make sure you select the correct options
+
+[.stretch]
+image::Ranking-node-sizes-by-centrality.png[align="center",title="Ranking node sizes by centrality"]
+
+
+== !
+
+[.stretch]
+image::Selecting-the-minimum-and-maximum-sizes-of-nodes.png[align="center",title="Selecting the minimum and maximum sizes of nodes"]
+
+
+== !
+
+[.stretch]
+image::ranking-centrality-miserables-3--en.png[align="center",title="Result of the ranking"]
+
+
+== !
+
+[.stretch]
+image::Resizing-labels-to-reflect-their-node's-size.png[align="center",title="Resizing labels to reflect their node's size"]
+
+
+== !
+[.stretch]
+image::result-label-resizing-en.png[align="center",title="Result of the label resizing"]
+
+
+
+== exporting a network as a picture
+
+== 1. exporting a screenshot from the Overview (a png image)
+
+== !
+
+[.stretch]
+image::Open-the-configuration-panel-for-screenshots.png[align="center",title="Open the configuration panel for screenshots"]
+
+
+== !
+
+Select the maximum value for anti-aliasing, and multiply values for width and height for higher resolution. For example, resolution x 3 is width = 3072 and height = 2304
+
+[.stretch]
+image::en/configuration-screenshot-en.png[align="center",title="The configuration panel for screenshots"]
+
+
+== 2. exporting a pdf or svg picture
+
+== !
+Let's switch to the preview panel:
+
+[.stretch]
+image::Switching-to-the-preview-panel.png[align="center",title="Switching to the preview panel"]
+
+
+== !
+The preview panel is dedicated to the preparation of the picture to be exported as a pdf or svg, which are "scalable": in pdf or sv, the resolution of the graph will remain perfect, even with a strong zoom.
+But as you see, it means the network is now looking different than in the Overview.
+
+== !
+CAUTION: contrary to the Overview panel, here you need to hit the "refresh" button after each parameter change.
+
+[.stretch]
+image::Updating-the-parameters.png[align="center",title="Updating the parameters"]
+
+
+== !
+Here I change just 2 parameters: `Show Labels` and `Font` (which I reduce to size "5"), to get:
+
+[.stretch]
+image::Result-of-preview.png[align="center",title="Result of preview"]
+
+
+== !
+Export: just click on the button and select the file format you prefer
+
+[.stretch]
+image::Export-button.png[align="center",title="Export button"]
+
+
+== donwload the result file
+
+link:../resources/miserables_result.zip[download this zip file] if you need to see the network in its final form.
+
+
+== The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+
+
+
+== questions and exercises
+
+== !
+1. Open the file `miserables.gexf` with a text editor (here is how to do it on a http://www.dummies.com/computers/macs/how-to-open-and-edit-a-text-file-on-a-mac/[Mac], and on https://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/app_notepad.mspx?mfr=true[Windows]). See how the nodes and the links are written in the file. Can you find the character Javert?
+
+== !
+[start=2]
+2. Our network of Les Miserables characters was undirected. Can you think of networks which are directed?
+
+Imagine how undirected and directed networks differ when computing centrality, for example.
+
+== !
+[start=3]
+3. Force Atlas 2 is a layout which brings together connected nodes, and spreads out unconnected nodes. We might have nodes with no relation at all with other nodes (called "isolated nodes").
+
+How will these isolated nodes move on screen?
+
+== !
+[start=4]
+4. When applying the Force Atlas 2 layout, the network moves quickly, then stabilizes, and then keeps moving a bit.
+
+Can you guess why this is happening?
+
+== !
+[start=5]
+5. In the list of layouts, Force Atlas 2 is just one of many options you can choose.
+Try "Fruchterman Reingold" and "Yfan Hu".
+
+These are layouts which follow the same logic as Force Atlas 2, but with slight variations. Explore how these algorithms result in similar, yet specific layouts.
+
+== !
+[start=6]
+6. In this tutorial, we defined degree centrality.
+Can you imagine a situation when a node with the largest degree centrality will actually be in the periphery of the network? You can draw a toy network to help you figure.
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_common.md b/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_common.md
new file mode 100644
index 0000000..00be60a
--- /dev/null
+++ b/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_common.md
@@ -0,0 +1,284 @@
+= Twitter Streaming Importer
+Clément Levallois ;Matthieu Totet
+2017-01-31
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Description of the plugin
+
+//ST: Author and documentation
+
+This plugin is created by https://matthieu-totet.fr[Matthieu Totet], who is a long time community member of Gephi (check his https://github.com/totetmatt/GephiStreamer[Gephi Streamer]!)
+
+You can find additional documentation on this plugin on https://matthieu-totet.fr/Koumin/2016/04/25/twitter-streaming-importer-naoyun-as-a-gephi-plugin/[Matthieu's website].
+
+//ST: Description of the plugin
+
+This plugin enables you to:
+
+* Collect tweets in realtime, on the topic you choose
+* Get the connections between the users mentionned in these tweets
+* Visualize these connections in Gephi, or just export all the tweets to Excel
+
+== Installing the plugin
+
+//ST: Installing the plugin
+
+//ST: !
+[WARNING]
+The setup of the plugin will need you to create a Twitter account, if you don't already have one.
+Get your Twitter login and password ready. **You also need a mobile phone number where your Twitter account can be confirmed**.
+
+//ST: !
+[CAUTION]
+====
+Setting up a Twitter account and creating a Twitter app is a boring task.
+
+It takes just 15 minutes, and you have to do it just once. Then you'll have Twitter at your fingertips.
+
+Let's go!
+====
+
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Initializing the plugin (to be done just once)
+
+//ST: Initializing the plugin (to be done just once)
+
+//ST: Creating a Twitter account
+
+//ST: !
+
+image::Open-a-new-project-in-Gephi.png[align="center", title="Open a new project in Gephi"]
+{nbsp} +
+
+//ST: !
+
+image::Display-the-plugin-in-Gephi.png[align="center", title="Display the plugin in Gephi"]
+{nbsp} +
+
+//ST: !
+We need now to connect to a Twitter account. If you already have a Twitter account and you are logged in, <>.
+
+//ST: !
+
+image::Creating-a-Twitter-account.png[align="center", title="Creating a Twitter account"]
+{nbsp} +
+
+//ST: !
+
+image::Confirmation-of-the-sign-up.png[align="center", title="Confirmation of the sign-up"]
+{nbsp} +
+
+//ST: !
+
+[WARNING]
+You have received an email by Twitter about the sign up. At some point (but now would be a good time), you must click on the link in this email to confirm it.
+
+
+//ST: !
+
+image::Enter-the-verification-code-you-received-by-sms.png[align="center", title="Enter the verification code you received by sms"]
+{nbsp} +
+
+
+//ST: !
+
+image::Choose-a-username.png[align="center", title="Choose a username"]
+{nbsp} +
+
+//ST: !
+
+The next screens of the sign up for Twitter are not important for us.
+Just follow the steps until you get to the page showing your Twitter profile, meaning the setup is finished.
+
+
+
+//ST: !
+
+[[twitter-account-finished-setup]]
+You now have a Twitter account.
+We need to create a Twitter app which will authorize us to collect tweets automatically via Gephi
+
+Go to *https://apps.twiter.com[https://apps.twiter.com]*:
+
+//ST: !
+
+image::Create-a-new-app.png[align="center", title="Create a new app"]
+{nbsp} +
+
+//ST: !
+
+image::Fill-in-the-fields.png[align="center", title="Fill in the fields"]
+{nbsp} +
+
+//ST: !
+
+INFO: Almost there!!
+
+//ST: !
+
+image::Click-on-Keys-and-Access-Tokens.png[align="center", title="Click on Keys and Access Tokens"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-Create-my-access-token.png[align="center", title="Click on Create my access token"]
+{nbsp} +
+
+//ST: !
+
+image::Copy-paste-these-codes-into-Gephi.png[align="center", title="Copy paste these codes into Gephi"]
+{nbsp} +
+
+//ST: !
+
+image::Where-to-copy-paste-the-codes-in-Gephi.png[align="center", title="Where to copy paste the codes in Gephi"]
+{nbsp} +
+
+//ST: Done!
+
+icon:thumbs-up[] Well done. This setup had just to be done once.
+Your credentials are saved and will be loaded at each run.
+Now we can start using the plugin.
+
+== Using the plugin
+
+//ST: Using the plugin
+
+//ST: !
+
+
+With the ** Words to follow ** tab, you will be able to follow one or multiple words. For ** hashtags **, just enter the word without the hash in front of it. (e.g if you want to follow '**#Gephi**' just add '**Gephi**')
+
+//ST: !
+
+With the ** Users to follow ** tab, you will be able to follow the activity of one or multiple users. Any tweet from this user or retweeting or mentioning the user will be captured.
+
+//ST: !
+
+You can combine the 2 tabs, the tweet collected will be matching either the Words to follow query **or** the User to follow query
+
+//ST: !
+
+The ** Load Query File** and ** Save Query File ** buttons are here to save your search queries in a file or to load it from a file. It's convenient if you have
+a long list of words / users.
+
+//ST: !
+
+==== Network Logic
+
+//ST: !
+
+
+A ** Network Logic ** means: what should be done with an incoming tweet? How to transform it as a set of nodes and edges?
+
+//ST: The dropdown menu to choose the network logic:
+
+image::Selection-of-the-network-logic.png[align="center",title="Selection of the network logic"]
+{nbsp} +
+
+There are for the moment 3 Network Logics to choose from:
+
+//ST: !
+
+* Full Twitter Network : This will represent **all** entities (User, Tweet, Hastags, URL, Media, Symbol etc...) as a graph.
+* User Network : This will represent the interaction between users. Any mentions, retweets or quotes between 2 users, will be represented. The size of the edge represent the number of interactions between 2 users.
+* Hashtag Network : This will create the network of hashtag.
+
+//ST: !
+In the following, we use the network logic "User Network":
+
+//ST: !
+
+image::Adding-terms-and-launching-the-collection-of-tweets.png[align="center", title="Adding terms and launching the collection of tweets"]
+{nbsp} +
+
+//ST: !
+
+Be careful that if you choose very common terms, tweets will arrive fast and in large volumes.
+
+If you don't have enough memory (RAM) on your computer, this could make it crash.
+
+Click on "disconnect" to stop the collection of tweets.
+
+
+//ST: !
+image::en/twitter-streaming-importer/result-plugin-1-en.png[align="center",title="Users mentioned in or retweeting tweets citing the search terms"]
+{nbsp} +
+
+
+//ST: Applying a layout while the tweets arrive.
+
+You can see the users organize spatially in real time, while the tweets are being collected.
+
+Just run Force Atlas 2 in the "Layout" panel. This will not interrupt the collection of tweets.
+
+//ST: !
+
+image::Running-the-Force-Atlas-layout-while-the-tweets-are-arriving.png[align="center", title="Running the Force Atlas layout while the tweets are arriving"]
+{nbsp} +
+
+//ST: !
+Shift to the `data laboratory` to view the data collected, in a spreadsheet format:
+
+image::Switching-to-the-data-laboratory-view.png[align="center", title="Switching to the data laboratory view"]
+{nbsp} +
+
+//ST: !
+There, you can export nodes and relations ("edges") as csv files by clicking on "Export table".
+
+//ST: !
+
+==== Timeline
+
+//ST: !
+
+When you are finished with your stream, you can use the ** timeline ** feature to replay the stream of data and look at a particular time window.
+
+_This feature is still experimental and is higly subject to bug._
+
+//ST: !
+
+image::en/twitter-streaming-importer/timeline-enable.png[align="center", title="How to activate the Timeline"]
+{nbsp} +
+
+//ST: !
+image::en/twitter-streaming-importer/timeline.gif[align="center", title="Example of Timeline"]
+{nbsp} +
+
+== The end
+
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_html.md b/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_html.md
new file mode 100644
index 0000000..61e442c
--- /dev/null
+++ b/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_html.md
@@ -0,0 +1,301 @@
+= Twitter Streaming Importer
+Clément Levallois ;Matthieu Totet
+2017-01-31
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Description of the plugin
+
+//ST: Author and documentation
+
+This plugin is created by https://matthieu-totet.fr[Matthieu Totet], who is a long time community member of Gephi (check his https://github.com/totetmatt/GephiStreamer[Gephi Streamer]!)
+
+You can find additional documentation on this plugin on https://matthieu-totet.fr/Koumin/2016/04/25/twitter-streaming-importer-naoyun-as-a-gephi-plugin/[Matthieu's website].
+
+//ST: Description of the plugin
+
+This plugin enables you to:
+
+* Collect tweets in realtime, on the topic you choose
+* Get the connections between the users mentionned in these tweets
+* Visualize these connections in Gephi, or just export all the tweets to Excel
+
+== Installing the plugin
+
+//ST: Installing the plugin
+
+//ST: !
+[WARNING]
+The setup of the plugin will need you to create a Twitter account, if you don't already have one.
+Get your Twitter login and password ready. **You also need a mobile phone number where your Twitter account can be confirmed**.
+
+//ST: !
+[CAUTION]
+====
+Setting up a Twitter account and creating a Twitter app is a boring task.
+
+It takes just 15 minutes, and you have to do it just once. Then you'll have Twitter at your fingertips.
+
+Let's go!
+====
+
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Initializing the plugin (to be done just once)
+
+//ST: Initializing the plugin (to be done just once)
+
+//ST: Creating a Twitter account
+
+//ST: !
+
+image::Open-a-new-project-in-Gephi.png[align="center", title="Open a new project in Gephi"]
+{nbsp} +
+
+//ST: !
+
+image::Display-the-plugin-in-Gephi.png[align="center", title="Display the plugin in Gephi"]
+{nbsp} +
+
+//ST: !
+We need now to connect to a Twitter account. If you already have a Twitter account and you are logged in, <>.
+
+//ST: !
+
+image::Creating-a-Twitter-account.png[align="center", title="Creating a Twitter account"]
+{nbsp} +
+
+//ST: !
+
+image::Confirmation-of-the-sign-up.png[align="center", title="Confirmation of the sign-up"]
+{nbsp} +
+
+//ST: !
+
+[WARNING]
+You have received an email by Twitter about the sign up. At some point (but now would be a good time), you must click on the link in this email to confirm it.
+
+
+//ST: !
+
+image::Enter-the-verification-code-you-received-by-sms.png[align="center", title="Enter the verification code you received by sms"]
+{nbsp} +
+
+
+//ST: !
+
+image::Choose-a-username.png[align="center", title="Choose a username"]
+{nbsp} +
+
+//ST: !
+
+The next screens of the sign up for Twitter are not important for us.
+Just follow the steps until you get to the page showing your Twitter profile, meaning the setup is finished.
+
+
+
+//ST: !
+
+[[twitter-account-finished-setup]]
+You now have a Twitter account.
+We need to create a Twitter app which will authorize us to collect tweets automatically via Gephi
+
+Go to *https://apps.twiter.com[https://apps.twiter.com]*:
+
+//ST: !
+
+image::Create-a-new-app.png[align="center", title="Create a new app"]
+{nbsp} +
+
+//ST: !
+
+image::Fill-in-the-fields.png[align="center", title="Fill in the fields"]
+{nbsp} +
+
+//ST: !
+
+INFO: Almost there!!
+
+//ST: !
+
+image::Click-on-Keys-and-Access-Tokens.png[align="center", title="Click on Keys and Access Tokens"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-Create-my-access-token.png[align="center", title="Click on Create my access token"]
+{nbsp} +
+
+//ST: !
+
+image::Copy-paste-these-codes-into-Gephi.png[align="center", title="Copy paste these codes into Gephi"]
+{nbsp} +
+
+//ST: !
+
+image::Where-to-copy-paste-the-codes-in-Gephi.png[align="center", title="Where to copy paste the codes in Gephi"]
+{nbsp} +
+
+//ST: Done!
+
+icon:thumbs-up[] Well done. This setup had just to be done once.
+Your credentials are saved and will be loaded at each run.
+Now we can start using the plugin.
+
+== Using the plugin
+
+//ST: Using the plugin
+
+//ST: !
+
+
+With the ** Words to follow ** tab, you will be able to follow one or multiple words. For ** hashtags **, just enter the word without the hash in front of it. (e.g if you want to follow '**#Gephi**' just add '**Gephi**')
+
+//ST: !
+
+With the ** Users to follow ** tab, you will be able to follow the activity of one or multiple users. Any tweet from this user or retweeting or mentioning the user will be captured.
+
+//ST: !
+
+You can combine the 2 tabs, the tweet collected will be matching either the Words to follow query **or** the User to follow query
+
+//ST: !
+
+The ** Load Query File** and ** Save Query File ** buttons are here to save your search queries in a file or to load it from a file. It's convenient if you have
+a long list of words / users.
+
+//ST: !
+
+==== Network Logic
+
+//ST: !
+
+
+A ** Network Logic ** means: what should be done with an incoming tweet? How to transform it as a set of nodes and edges?
+
+//ST: The dropdown menu to choose the network logic:
+
+image::Selection-of-the-network-logic.png[align="center",title="Selection of the network logic"]
+{nbsp} +
+
+There are for the moment 3 Network Logics to choose from:
+
+//ST: !
+
+* Full Twitter Network : This will represent **all** entities (User, Tweet, Hastags, URL, Media, Symbol etc...) as a graph.
+* User Network : This will represent the interaction between users. Any mentions, retweets or quotes between 2 users, will be represented. The size of the edge represent the number of interactions between 2 users.
+* Hashtag Network : This will create the network of hashtag.
+
+//ST: !
+In the following, we use the network logic "User Network":
+
+//ST: !
+
+image::Adding-terms-and-launching-the-collection-of-tweets.png[align="center", title="Adding terms and launching the collection of tweets"]
+{nbsp} +
+
+//ST: !
+
+Be careful that if you choose very common terms, tweets will arrive fast and in large volumes.
+
+If you don't have enough memory (RAM) on your computer, this could make it crash.
+
+Click on "disconnect" to stop the collection of tweets.
+
+
+//ST: !
+image::en/twitter-streaming-importer/result-plugin-1-en.png[align="center",title="Users mentioned in or retweeting tweets citing the search terms"]
+{nbsp} +
+
+
+//ST: Applying a layout while the tweets arrive.
+
+You can see the users organize spatially in real time, while the tweets are being collected.
+
+Just run Force Atlas 2 in the "Layout" panel. This will not interrupt the collection of tweets.
+
+//ST: !
+
+image::Running-the-Force-Atlas-layout-while-the-tweets-are-arriving.png[align="center", title="Running the Force Atlas layout while the tweets are arriving"]
+{nbsp} +
+
+//ST: !
+Shift to the `data laboratory` to view the data collected, in a spreadsheet format:
+
+image::Switching-to-the-data-laboratory-view.png[align="center", title="Switching to the data laboratory view"]
+{nbsp} +
+
+//ST: !
+There, you can export nodes and relations ("edges") as csv files by clicking on "Export table".
+
+//ST: !
+
+==== Timeline
+
+//ST: !
+
+When you are finished with your stream, you can use the ** timeline ** feature to replay the stream of data and look at a particular time window.
+
+_This feature is still experimental and is higly subject to bug._
+
+//ST: !
+
+image::en/twitter-streaming-importer/timeline-enable.png[align="center", title="How to activate the Timeline"]
+{nbsp} +
+
+//ST: !
+image::en/twitter-streaming-importer/timeline.gif[align="center", title="Example of Timeline"]
+{nbsp} +
+
+== The end
+
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_pdf.md b/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_pdf.md
new file mode 100644
index 0000000..f2a84bd
--- /dev/null
+++ b/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_pdf.md
@@ -0,0 +1,284 @@
+= Twitter Streaming Importer
+Clément Levallois ;Matthieu Totet
+2017-01-31
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== Description of the plugin
+
+//ST: Author and documentation
+
+This plugin is created by https://matthieu-totet.fr[Matthieu Totet], who is a long time community member of Gephi (check his https://github.com/totetmatt/GephiStreamer[Gephi Streamer]!)
+
+You can find additional documentation on this plugin on https://matthieu-totet.fr/Koumin/2016/04/25/twitter-streaming-importer-naoyun-as-a-gephi-plugin/[Matthieu's website].
+
+//ST: Description of the plugin
+
+This plugin enables you to:
+
+* Collect tweets in realtime, on the topic you choose
+* Get the connections between the users mentionned in these tweets
+* Visualize these connections in Gephi, or just export all the tweets to Excel
+
+== Installing the plugin
+
+//ST: Installing the plugin
+
+//ST: !
+[WARNING]
+The setup of the plugin will need you to create a Twitter account, if you don't already have one.
+Get your Twitter login and password ready. **You also need a mobile phone number where your Twitter account can be confirmed**.
+
+//ST: !
+[CAUTION]
+====
+Setting up a Twitter account and creating a Twitter app is a boring task.
+
+It takes just 15 minutes, and you have to do it just once. Then you'll have Twitter at your fingertips.
+
+Let's go!
+====
+
+//ST: !
+
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+{nbsp} +
+
+
+//ST: !
+
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+{nbsp} +
+
+//ST: !
+
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+{nbsp} +
+
+== Initializing the plugin (to be done just once)
+
+//ST: Initializing the plugin (to be done just once)
+
+//ST: Creating a Twitter account
+
+//ST: !
+
+image::Open-a-new-project-in-Gephi.png[align="center", title="Open a new project in Gephi"]
+{nbsp} +
+
+//ST: !
+
+image::Display-the-plugin-in-Gephi.png[align="center", title="Display the plugin in Gephi"]
+{nbsp} +
+
+//ST: !
+We need now to connect to a Twitter account. If you already have a Twitter account and you are logged in, <>.
+
+//ST: !
+
+image::Creating-a-Twitter-account.png[align="center", title="Creating a Twitter account"]
+{nbsp} +
+
+//ST: !
+
+image::Confirmation-of-the-sign-up.png[align="center", title="Confirmation of the sign-up"]
+{nbsp} +
+
+//ST: !
+
+[WARNING]
+You have received an email by Twitter about the sign up. At some point (but now would be a good time), you must click on the link in this email to confirm it.
+
+
+//ST: !
+
+image::Enter-the-verification-code-you-received-by-sms.png[align="center", title="Enter the verification code you received by sms"]
+{nbsp} +
+
+
+//ST: !
+
+image::Choose-a-username.png[align="center", title="Choose a username"]
+{nbsp} +
+
+//ST: !
+
+The next screens of the sign up for Twitter are not important for us.
+Just follow the steps until you get to the page showing your Twitter profile, meaning the setup is finished.
+
+
+
+//ST: !
+
+[[twitter-account-finished-setup]]
+You now have a Twitter account.
+We need to create a Twitter app which will authorize us to collect tweets automatically via Gephi
+
+Go to *https://apps.twiter.com[https://apps.twiter.com]*:
+
+//ST: !
+
+image::Create-a-new-app.png[align="center", title="Create a new app"]
+{nbsp} +
+
+//ST: !
+
+image::Fill-in-the-fields.png[align="center", title="Fill in the fields"]
+{nbsp} +
+
+//ST: !
+
+INFO: Almost there!!
+
+//ST: !
+
+image::Click-on-Keys-and-Access-Tokens.png[align="center", title="Click on Keys and Access Tokens"]
+{nbsp} +
+
+//ST: !
+
+image::Click-on-Create-my-access-token.png[align="center", title="Click on Create my access token"]
+{nbsp} +
+
+//ST: !
+
+image::Copy-paste-these-codes-into-Gephi.png[align="center", title="Copy paste these codes into Gephi"]
+{nbsp} +
+
+//ST: !
+
+image::Where-to-copy-paste-the-codes-in-Gephi.png[align="center", title="Where to copy paste the codes in Gephi"]
+{nbsp} +
+
+//ST: Done!
+
+icon:thumbs-up[] Well done. This setup had just to be done once.
+Your credentials are saved and will be loaded at each run.
+Now we can start using the plugin.
+
+== Using the plugin
+
+//ST: Using the plugin
+
+//ST: !
+
+
+With the ** Words to follow ** tab, you will be able to follow one or multiple words. For ** hashtags **, just enter the word without the hash in front of it. (e.g if you want to follow '**#Gephi**' just add '**Gephi**')
+
+//ST: !
+
+With the ** Users to follow ** tab, you will be able to follow the activity of one or multiple users. Any tweet from this user or retweeting or mentioning the user will be captured.
+
+//ST: !
+
+You can combine the 2 tabs, the tweet collected will be matching either the Words to follow query **or** the User to follow query
+
+//ST: !
+
+The ** Load Query File** and ** Save Query File ** buttons are here to save your search queries in a file or to load it from a file. It's convenient if you have
+a long list of words / users.
+
+//ST: !
+
+==== Network Logic
+
+//ST: !
+
+
+A ** Network Logic ** means: what should be done with an incoming tweet? How to transform it as a set of nodes and edges?
+
+//ST: The dropdown menu to choose the network logic:
+
+image::Selection-of-the-network-logic.png[align="center",title="Selection of the network logic"]
+{nbsp} +
+
+There are for the moment 3 Network Logics to choose from:
+
+//ST: !
+
+* Full Twitter Network : This will represent **all** entities (User, Tweet, Hastags, URL, Media, Symbol etc...) as a graph.
+* User Network : This will represent the interaction between users. Any mentions, retweets or quotes between 2 users, will be represented. The size of the edge represent the number of interactions between 2 users.
+* Hashtag Network : This will create the network of hashtag.
+
+//ST: !
+In the following, we use the network logic "User Network":
+
+//ST: !
+
+image::Adding-terms-and-launching-the-collection-of-tweets.png[align="center", title="Adding terms and launching the collection of tweets"]
+{nbsp} +
+
+//ST: !
+
+Be careful that if you choose very common terms, tweets will arrive fast and in large volumes.
+
+If you don't have enough memory (RAM) on your computer, this could make it crash.
+
+Click on "disconnect" to stop the collection of tweets.
+
+
+//ST: !
+image::en/twitter-streaming-importer/result-plugin-1-en.png[align="center",title="Users mentioned in or retweeting tweets citing the search terms"]
+{nbsp} +
+
+
+//ST: Applying a layout while the tweets arrive.
+
+You can see the users organize spatially in real time, while the tweets are being collected.
+
+Just run Force Atlas 2 in the "Layout" panel. This will not interrupt the collection of tweets.
+
+//ST: !
+
+image::Running-the-Force-Atlas-layout-while-the-tweets-are-arriving.png[align="center", title="Running the Force Atlas layout while the tweets are arriving"]
+{nbsp} +
+
+//ST: !
+Shift to the `data laboratory` to view the data collected, in a spreadsheet format:
+
+image::Switching-to-the-data-laboratory-view.png[align="center", title="Switching to the data laboratory view"]
+{nbsp} +
+
+//ST: !
+There, you can export nodes and relations ("edges") as csv files by clicking on "Export table".
+
+//ST: !
+
+==== Timeline
+
+//ST: !
+
+When you are finished with your stream, you can use the ** timeline ** feature to replay the stream of data and look at a particular time window.
+
+_This feature is still experimental and is higly subject to bug._
+
+//ST: !
+
+image::en/twitter-streaming-importer/timeline-enable.png[align="center", title="How to activate the Timeline"]
+{nbsp} +
+
+//ST: !
+
+{nbsp} +
+
+== The end
+
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_slides.md b/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_slides.md
new file mode 100644
index 0000000..9ec5727
--- /dev/null
+++ b/docs/generated-pdf/subdir/twitter-streaming-importer-en_temp_slides.md
@@ -0,0 +1,318 @@
+= Twitter Streaming Importer
+Clément Levallois ;Matthieu Totet
+2017-01-31
+
+last modified: {docdate}
+
+:icons: font
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+
+== Author and documentation
+
+This plugin is created by https://matthieu-totet.fr[Matthieu Totet], who is a long time community member of Gephi (check his https://github.com/totetmatt/GephiStreamer[Gephi Streamer]!)
+
+You can find additional documentation on this plugin on https://matthieu-totet.fr/Koumin/2016/04/25/twitter-streaming-importer-naoyun-as-a-gephi-plugin/[Matthieu's website].
+
+== Description of the plugin
+
+This plugin enables you to:
+
+* Collect tweets in realtime, on the topic you choose
+* Get the connections between the users mentionned in these tweets
+* Visualize these connections in Gephi, or just export all the tweets to Excel
+
+
+== Installing the plugin
+
+== !
+[WARNING]
+The setup of the plugin will need you to create a Twitter account, if you don't already have one.
+Get your Twitter login and password ready. **You also need a mobile phone number where your Twitter account can be confirmed**.
+
+== !
+[CAUTION]
+====
+Setting up a Twitter account and creating a Twitter app is a boring task.
+
+It takes just 15 minutes, and you have to do it just once. Then you'll have Twitter at your fingertips.
+
+Let's go!
+====
+
+== !
+
+[.stretch]
+image::Choose-the-menu-Tools-then-Plugins.png[align="center", title="Choose the menu Tools then Plugins"]
+
+
+
+== !
+
+[.stretch]
+image::Click-on-the-tab-Available-Plugins.png[align="center", title="Click on the tab Available Plugins"]
+
+
+== !
+
+[.stretch]
+image::Install-the-plugin-then-restart-Gephi.png[align="center", title="Install the plugin then restart Gephi"]
+
+
+
+== Initializing the plugin (to be done just once)
+
+== Creating a Twitter account
+
+== !
+
+[.stretch]
+image::Open-a-new-project-in-Gephi.png[align="center", title="Open a new project in Gephi"]
+
+
+== !
+
+[.stretch]
+image::Display-the-plugin-in-Gephi.png[align="center", title="Display the plugin in Gephi"]
+
+
+== !
+We need now to connect to a Twitter account. If you already have a Twitter account and you are logged in, <>.
+
+== !
+
+[.stretch]
+image::Creating-a-Twitter-account.png[align="center", title="Creating a Twitter account"]
+
+
+== !
+
+[.stretch]
+image::Confirmation-of-the-sign-up.png[align="center", title="Confirmation of the sign-up"]
+
+
+== !
+
+[WARNING]
+You have received an email by Twitter about the sign up. At some point (but now would be a good time), you must click on the link in this email to confirm it.
+
+
+== !
+
+[.stretch]
+image::Enter-the-verification-code-you-received-by-sms.png[align="center", title="Enter the verification code you received by sms"]
+
+
+
+== !
+
+[.stretch]
+image::Choose-a-username.png[align="center", title="Choose a username"]
+
+
+== !
+
+The next screens of the sign up for Twitter are not important for us.
+Just follow the steps until you get to the page showing your Twitter profile, meaning the setup is finished.
+
+
+
+== !
+
+[[twitter-account-finished-setup]]
+You now have a Twitter account.
+We need to create a Twitter app which will authorize us to collect tweets automatically via Gephi
+
+Go to *https://apps.twiter.com[https://apps.twiter.com]*:
+
+== !
+
+[.stretch]
+image::Create-a-new-app.png[align="center", title="Create a new app"]
+
+
+== !
+
+[.stretch]
+image::Fill-in-the-fields.png[align="center", title="Fill in the fields"]
+
+
+== !
+
+INFO: Almost there!!
+
+== !
+
+[.stretch]
+image::Click-on-Keys-and-Access-Tokens.png[align="center", title="Click on Keys and Access Tokens"]
+
+
+== !
+
+[.stretch]
+image::Click-on-Create-my-access-token.png[align="center", title="Click on Create my access token"]
+
+
+== !
+
+[.stretch]
+image::Copy-paste-these-codes-into-Gephi.png[align="center", title="Copy paste these codes into Gephi"]
+
+
+== !
+
+[.stretch]
+image::Where-to-copy-paste-the-codes-in-Gephi.png[align="center", title="Where to copy paste the codes in Gephi"]
+
+
+== Done!
+
+icon:thumbs-up[] Well done. This setup had just to be done once.
+Your credentials are saved and will be loaded at each run.
+Now we can start using the plugin.
+
+
+== Using the plugin
+
+== !
+
+
+With the ** Words to follow ** tab, you will be able to follow one or multiple words. For ** hashtags **, just enter the word without the hash in front of it. (e.g if you want to follow '**#Gephi**' just add '**Gephi**')
+
+== !
+
+With the ** Users to follow ** tab, you will be able to follow the activity of one or multiple users. Any tweet from this user or retweeting or mentioning the user will be captured.
+
+== !
+
+You can combine the 2 tabs, the tweet collected will be matching either the Words to follow query **or** the User to follow query
+
+== !
+
+The ** Load Query File** and ** Save Query File ** buttons are here to save your search queries in a file or to load it from a file. It's convenient if you have
+a long list of words / users.
+
+== !
+
+==== Network Logic
+
+== !
+
+
+A ** Network Logic ** means: what should be done with an incoming tweet? How to transform it as a set of nodes and edges?
+
+== The dropdown menu to choose the network logic:
+
+[.stretch]
+image::Selection-of-the-network-logic.png[align="center",title="Selection of the network logic"]
+
+
+There are for the moment 3 Network Logics to choose from:
+
+== !
+
+* Full Twitter Network : This will represent **all** entities (User, Tweet, Hastags, URL, Media, Symbol etc...) as a graph.
+* User Network : This will represent the interaction between users. Any mentions, retweets or quotes between 2 users, will be represented. The size of the edge represent the number of interactions between 2 users.
+* Hashtag Network : This will create the network of hashtag.
+
+== !
+In the following, we use the network logic "User Network":
+
+== !
+
+[.stretch]
+image::Adding-terms-and-launching-the-collection-of-tweets.png[align="center", title="Adding terms and launching the collection of tweets"]
+
+
+== !
+
+Be careful that if you choose very common terms, tweets will arrive fast and in large volumes.
+
+If you don't have enough memory (RAM) on your computer, this could make it crash.
+
+Click on "disconnect" to stop the collection of tweets.
+
+
+== !
+[.stretch]
+image::en/twitter-streaming-importer/result-plugin-1-en.png[align="center",title="Users mentioned in or retweeting tweets citing the search terms"]
+
+
+
+== Applying a layout while the tweets arrive.
+
+You can see the users organize spatially in real time, while the tweets are being collected.
+
+Just run Force Atlas 2 in the "Layout" panel. This will not interrupt the collection of tweets.
+
+== !
+
+[.stretch]
+image::Running-the-Force-Atlas-layout-while-the-tweets-are-arriving.png[align="center", title="Running the Force Atlas layout while the tweets are arriving"]
+
+
+== !
+Shift to the `data laboratory` to view the data collected, in a spreadsheet format:
+
+[.stretch]
+image::Switching-to-the-data-laboratory-view.png[align="center", title="Switching to the data laboratory view"]
+
+
+== !
+There, you can export nodes and relations ("edges") as csv files by clicking on "Export table".
+
+== !
+
+==== Timeline
+
+== !
+
+When you are finished with your stream, you can use the ** timeline ** feature to replay the stream of data and look at a particular time window.
+
+_This feature is still experimental and is higly subject to bug._
+
+== !
+
+[.stretch]
+image::en/twitter-streaming-importer/timeline-enable.png[align="center", title="How to activate the Timeline"]
+
+
+== !
+[.stretch]
+image::en/twitter-streaming-importer/timeline.gif[align="center", title="Example of Timeline"]
+
+
+
+== The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/using-filters-en_temp_common.md b/docs/generated-pdf/subdir/using-filters-en_temp_common.md
new file mode 100644
index 0000000..7dac59f
--- /dev/null
+++ b/docs/generated-pdf/subdir/using-filters-en_temp_common.md
@@ -0,0 +1,438 @@
+= Using filters
+Clément Levallois
+2017-02-10
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== download a network file for practice
+//ST: download a network file for practice
+
+//ST: !
+
+link:../resources/miserables.zip[download this zip file] and unzip it on your computer.
+
+or use this direct link: https://tinyurl.com/gephi-tuto-3[https://tinyurl.com/gephi-tuto-3]
+
+You should find the file `miserables.gexf` in it. Save it in a folder you will remember (or create a folder specially for this small project).
+
+//ST: description of the file / the network
+//ST: !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+
+== open the network in Gephi
+//ST: open the network in Gephi
+
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables.gexf` on your computer and open it
+
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+{nbsp} +
+
+//ST: !
+A report window will open, giving you basic info on the network you opened:
+
+image::en/opening-file-report-en.png[align="center", title="report window"]
+{nbsp} +
+
+//ST: !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+//ST: !
+image::result_miserables.png[align="center",title="The network we will use"]
+{nbsp} +
+
+== getting a sense of the attributes in the data laboratory
+//ST: getting a sense of the attributes in the data laboratory
+
+//ST: !
+We can switch to the data laboratory to see the underlying data:
+
+image::Switching-to-the-data-laboratory.png[align="center",title="Switching to the data laboratory"]
+{nbsp} +
+
+
+//ST: !
+We see that the nodes of the network have many attributes. In particular, each have a Gender and a measure of how central they are:
+
+image::Nodes-attributes..png[align="center",title="Nodes attributes."]
+{nbsp} +
+
+//ST: !
+This is the list of edges (relations) in the network. Notice that they have a "weight" (a "strength").
+
+image::Edges-attributes..png[align="center",title="Edges attributes".]
+{nbsp} +
+
+== discovering the filter panel
+//ST: discovering the filter panel
+
+//ST: !
+In the overview, make sure the Filter panel is displayed:
+
+image::Making-the-Filter-panel-visible..png[align="center",title="Making the Filter panel visible."]
+{nbsp} +
+
+
+//ST: !
+How the Filter panel works:
+
+image::Workflow-of-filters--size=stretch.png[align="center",title="Workflow of filters", size="stretch"]
+{nbsp} +
+
+
+//ST: !
+An example: hiding edges with weight lower than 2
+
+image::en/filter-edge-weight-en.gif[align="center",title="How to use filters."]
+{nbsp} +
+
+//PDF: image::en/filter-edge-weight-1-en.png[align="center",title="Filtering out edges with weight lower than 2."]
+
+//PDF: {github-root}images/en/filter-edge-weight-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-2
+
+
+//ST: !
+When you are finished using a filter in the zone, right click on it and select "remove".
+
+== combining 2 filters
+//ST: combining 2 filters
+
+//ST: !
+One filter is applied AFTER this other:
+
+*The first filter to be applied is NESTED (placed inside) the second one as a "subfilter"*
+
+Which filter should be placed inside which? Let's look at different examples:
+
+//ST: !
+==== 1. Case when the placement of filters makes no difference
+
+//ST: !
+Goal: Keeping on screen only the female characters which have a tie (an edge, a relation) of at least strength 2.
+
+-> place the filter "edge weight" inside the filter "Gender":
+
+//ST: !
+
+image::Filter-on-the-Gender-attribute.png[align="center",title="Filter on the Gender attribute"]
+{nbsp} +
+
+//ST: !
+
+image::Filter-on-edge-weight.png[align="center",title="Filter on edge weight"]
+{nbsp} +
+
+//ST: !
+
+image::en/filter-edge-weight-gender-partition-en.gif[align="center",title="Keeping only female characters with at least 2 ties"]
+{nbsp} +
+
+//PDF: image::en/filter-edge-weight-gender-partition1-en.png[align="center",title="Keeping only female characters with at least 2 ties"]
+
+//PDF: {github-root}images/en/filter-edge-weight-gender-partition-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-1
+
+//ST: !
+In this case, it was equivalent to:
+
+- nest the "Gender" filter inside the "Edge weight" filter
+or
+- nest the "Edge weight" filter inside the "Gender" Filter
+
+-> The result was the same (the network on screen is identical in both cases)
+
+//ST: !
+==== 2. Case when the placement of filters makes a difference
+
+//ST: !
+Here, we want to visualize:
+
+- only the nodes which have *less than* 10 relations <1>
+- and among these, only those which form the "main island" of the network (we want to hide small detached groups of nodes) <2>
+
+<1> in technical terms, nodes with a `degree` of less than 10.
+<2> in technical terms, we are looking for the `giant component`
+
+//ST: !
+
+image::en/filter-degree-range-1-en.png[align="center",title="Filter on degree"]
+{nbsp} +
+
+//ST: !
+
+image::en/filter-giant-component-1-en.png[align="center",title="Filter on giant component"]
+{nbsp} +
+
+//ST: !
+We will see that the placement on the filters in the zone will make a difference.
+
+First, let us place the filter on giant component *inside* the filter on degree:
+
+//ST: !
+
+image::en/filter-order-1-en.png[align="center",title="Filters in one configuration"]
+{nbsp} +
+
+//ST: !
+In this first case,
+
+- only the giant component of the network was made visible.
+
+-> Since the network was just one big connected "island" to start with, it did not change a thing.
+
+- then, all characters with more than 10 relations where hidden
+
+-> this hides nodes which were connecting with many others, so that we end up with many groups, disconnected from each others.
+
+//ST: !
+
+Now instead, placing the filter degree *inside* the filter on giant component:
+
+image::en/filter-order-2-en.png[align="center",title="Same filters in another configuration"]
+{nbsp} +
+
+//ST: !
+In this second case,
+
+- starting from the complete network, all characters with more than 10 relations where deleted.
+
+-> this created a network made of many disconnected groups of nodes
+
+- then the giant component filter is applied,
+
+-> which had for effect to hide small groups, to keep in view only the biggest group of connected nodes.
+
+//ST: !
+
+WARNING: In summary: be careful how you apply several filters at once, this might have an effect on the logic of filtering.
+
+== filter operators
+//ST: Filter operators
+
+//ST: !
+==== 1. The MASK operator
+
+//ST: !
+Imagine you are interested in the female characters of the novel "Les Miserables".
+
+- you are interested in female characters and the relations among them
+- you are interested in the relations between female characters and male characters
+- you are *not* interested in the relations between male characters
+
+How to display this?
+
+//ST: !
+The MASK operator applied on the gender partition filter enables you to:
+
+- show all characters
+- relations between female characters
+- _and relations between male and female characters_
+- _but masking male-male relations_
+
+//ST: !
+
+image::en/operator-mask-1-en.png[align="center",title="Using the MASK operator"]
+{nbsp} +
+
+//ST: !
+It is also possible to hide / show only some of the directed relations between the visible graph and the filtered out graph:
+
+image::en/operator-mask-2-en.png[align="center",title="Parameters of the MASK operator"]
+{nbsp} +
+
+//ST: !
+==== 2. The UNION operator
+
+//ST: !
+
+Imagine you are interested in the characters with names starting with "L" or "J" in "Les Miserables".
+
+How to display only these characters?
+
+//ST: !
+We will need to apply filters on the `Label` of the nodes, which contains the names of the characters.
+
+However, looking at the "catalogue" of filters, we see no filter on `Label`. The reason is that `Label` is an internal property of nodes, inaccessible to filters.
+
+So we must first copy the Labels of the nodes in a new attribute, which we will be able to apply a filter on.
+
+Let's switch to the data laboratory and add this attribute:
+
+//ST: !
+
+image::Adding-a-column-for-Names.png[align="center",title="Adding a column for Names"]
+{nbsp} +
+
+
+//ST: !
+
+image::Copying-to-this-new-column.png[align="center",title="Copying to this new column"]
+{nbsp} +
+
+//ST: !
+We now have an attribute called "Name" that we can find in the Filters:
+
+image::New-filter-available.png[align="center",title="New filter available"]
+{nbsp} +
+
+//ST: !
+This is how the filter on Name and its parameters look like in the zone:
+
+image::en/filter-name-1-en.png[align="center",title="Name Filter"]
+{nbsp} +
+
+//ST: !
+To recall, we want to show only the characters which name start with "L" or "J". Let's start with the "L" characters.
+
+We need to find the names which match the pattern *`Start with an L`*. The way to describe a pattern in text is called a "regular expression".
+
+Said differently, _a regular expressions (also called "regex") is a convenient way to express a pattern we search for in a text_.
+
+//ST: !
+Regular expressions can become very sophisticated. But here, we need just a simple one:
+
+[source,regex]
+L.*
+
+Let's examine what the L, the dot and the star mean.
+
+//ST: !
+
+- the letter "L" means we want names starting with this first letter
+- . the dot means: any character
+- * the star means: the previous character, repeated any time.
+
+So: "select nodes which have a name starting with L, followed by any character, in any number"
+
+//ST: !
+Please note that you need to check the box "regex":
+
+image::en/filter-name-2-en.png[align="center",title="Using a regular expression in a filter"]
+{nbsp} +
+
+//ST: !
+When the filter is applied, only the characters wit a name starting with L will be displayed:
+
+image::en/filter-name-3-en.png[align="center",title="Using a regular expression in a filter"]
+{nbsp} +
+
+//ST: !
+How to filter characters with a name starting with the letter "L" or "J"?
+
+We could rely on a more complex regular expression to do this:
+
+[source,regex]
+[LJ].*
+
+Meaning: "select nodes which have a name starting with L or J, followed by any characters"
+
+//ST: !
+But we can also rely on 2 filters: one for L, one for J. Nesting one inside another would not work, it would mean:
+
+"show nodes which start with an L, and among them, only those which start with a J"
+
+-> no node can meet this condition, so they would all be invisible.
+
+//ST: !
+Instead, we should use the *`UNION`* operator that can be found here:
+
+image::en/filter-operator-union-1-en.png[align="center",title="The UNION operator in filters"]
+{nbsp} +
+
+//ST: !
+Drag it to the zone, and then drag inside it twice the `Attributes -> Equal -> Name` filter:
+
+image::en/filter-operator-union-2-en.png[align="center",title="The UNION operator and 2 subfilters"]
+{nbsp} +
+
+//ST: !
+In the settings of the first Name filter, put the regular expression:
+[source,regex]
+L.*
+
+In the second Name filter, put:
+[source,regex]
+J.*
+
+(make sure the "regex" box is checked in both cases)
+
+//ST: !
+As a result, the nodes selected by both filters are added up in the display:
+
+image::en/filter-operator-union-3-en.png[align="center",title="The UNION operator and 2 subfilters"]
+{nbsp} +
+
+//ST: !
+==== 3. The NOT operator
+
+//ST: !
+The NOT operator flips the result of a filter: what was hidden becomes visible and vice and versa.
+
+//ST: !
+Example: if we want to display all characters except for those returned by a UNION on 2 Name filters on L and J initials:
+
+image::en/filter-operator-not-3-en.png[align="center",title="The NOT nodes operator - 1"]
+{nbsp} +
+
+//ST: !
+Same effect, but applying the NOT operator on single filter using a regex on L or J:
+
+image::en/filter-operator-not-1-en.png[align="center",title="The NOT nodes operator - 2"]
+{nbsp} +
+
+//ST: !
+Same effect again, achieved without using the NOT operator. In regular expressions the ^ sign inside square brackets means "NOT":
+
+[source,regex]
+[^LJ].*
+
+image::en/filter-operator-not-2-en.png[align="center",title="Achieving a NOT effect with regex"]
+{nbsp} +
+
+//ST: !
+Tutorials about regular expressions:
+
+- http://www.regular-expressions.info/quickstart.html[https://regexone.com/]
+- http://www.themacroscope.org/?page_id=643[http://www.themacroscope.org/?page_id=643]
+
+And a web page where you can test your regular expressions: http://regexpal.com[http://regexpal.com]
+
+== more tutorials on using filters in Gephi
+//ST: More tutorials on using filters in Gephi
+
+//ST: !
+
+- https://www.youtube.com/watch?v=UrrWA_t1rjc[Video on using filters by Jen Golbeck]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
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+= Using filters
+Clément Levallois
+2017-02-10
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== download a network file for practice
+//ST: download a network file for practice
+
+//ST: !
+
+link:../resources/miserables.zip[download this zip file] and unzip it on your computer.
+
+or use this direct link: https://tinyurl.com/gephi-tuto-3[https://tinyurl.com/gephi-tuto-3]
+
+You should find the file `miserables.gexf` in it. Save it in a folder you will remember (or create a folder specially for this small project).
+
+//ST: description of the file / the network
+//ST: !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+
+== open the network in Gephi
+//ST: open the network in Gephi
+
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables.gexf` on your computer and open it
+
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+{nbsp} +
+
+//ST: !
+A report window will open, giving you basic info on the network you opened:
+
+image::en/opening-file-report-en.png[align="center", title="report window"]
+{nbsp} +
+
+//ST: !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+//ST: !
+image::result_miserables.png[align="center",title="The network we will use"]
+{nbsp} +
+
+== getting a sense of the attributes in the data laboratory
+//ST: getting a sense of the attributes in the data laboratory
+
+//ST: !
+We can switch to the data laboratory to see the underlying data:
+
+image::Switching-to-the-data-laboratory.png[align="center",title="Switching to the data laboratory"]
+{nbsp} +
+
+
+//ST: !
+We see that the nodes of the network have many attributes. In particular, each have a Gender and a measure of how central they are:
+
+image::Nodes-attributes..png[align="center",title="Nodes attributes."]
+{nbsp} +
+
+//ST: !
+This is the list of edges (relations) in the network. Notice that they have a "weight" (a "strength").
+
+image::Edges-attributes..png[align="center",title="Edges attributes".]
+{nbsp} +
+
+== discovering the filter panel
+//ST: discovering the filter panel
+
+//ST: !
+In the overview, make sure the Filter panel is displayed:
+
+image::Making-the-Filter-panel-visible..png[align="center",title="Making the Filter panel visible."]
+{nbsp} +
+
+
+//ST: !
+How the Filter panel works:
+
+image::Workflow-of-filters--size=stretch.png[align="center",title="Workflow of filters", size="stretch"]
+{nbsp} +
+
+
+//ST: !
+An example: hiding edges with weight lower than 2
+
+image::en/filter-edge-weight-en.gif[align="center",title="How to use filters."]
+{nbsp} +
+
+//PDF: image::en/filter-edge-weight-1-en.png[align="center",title="Filtering out edges with weight lower than 2."]
+
+//PDF: {github-root}images/en/filter-edge-weight-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-2
+
+
+//ST: !
+When you are finished using a filter in the zone, right click on it and select "remove".
+
+== combining 2 filters
+//ST: combining 2 filters
+
+//ST: !
+One filter is applied AFTER this other:
+
+*The first filter to be applied is NESTED (placed inside) the second one as a "subfilter"*
+
+Which filter should be placed inside which? Let's look at different examples:
+
+//ST: !
+==== 1. Case when the placement of filters makes no difference
+
+//ST: !
+Goal: Keeping on screen only the female characters which have a tie (an edge, a relation) of at least strength 2.
+
+-> place the filter "edge weight" inside the filter "Gender":
+
+//ST: !
+
+image::Filter-on-the-Gender-attribute.png[align="center",title="Filter on the Gender attribute"]
+{nbsp} +
+
+//ST: !
+
+image::Filter-on-edge-weight.png[align="center",title="Filter on edge weight"]
+{nbsp} +
+
+//ST: !
+
+image::en/filter-edge-weight-gender-partition-en.gif[align="center",title="Keeping only female characters with at least 2 ties"]
+{nbsp} +
+
+//PDF: image::en/filter-edge-weight-gender-partition1-en.png[align="center",title="Keeping only female characters with at least 2 ties"]
+
+//PDF: {github-root}images/en/filter-edge-weight-gender-partition-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-1
+
+//ST: !
+In this case, it was equivalent to:
+
+- nest the "Gender" filter inside the "Edge weight" filter
+or
+- nest the "Edge weight" filter inside the "Gender" Filter
+
+-> The result was the same (the network on screen is identical in both cases)
+
+//ST: !
+==== 2. Case when the placement of filters makes a difference
+
+//ST: !
+Here, we want to visualize:
+
+- only the nodes which have *less than* 10 relations <1>
+- and among these, only those which form the "main island" of the network (we want to hide small detached groups of nodes) <2>
+
+<1> in technical terms, nodes with a `degree` of less than 10.
+<2> in technical terms, we are looking for the `giant component`
+
+//ST: !
+
+image::en/filter-degree-range-1-en.png[align="center",title="Filter on degree"]
+{nbsp} +
+
+//ST: !
+
+image::en/filter-giant-component-1-en.png[align="center",title="Filter on giant component"]
+{nbsp} +
+
+//ST: !
+We will see that the placement on the filters in the zone will make a difference.
+
+First, let us place the filter on giant component *inside* the filter on degree:
+
+//ST: !
+
+image::en/filter-order-1-en.png[align="center",title="Filters in one configuration"]
+{nbsp} +
+
+//ST: !
+In this first case,
+
+- only the giant component of the network was made visible.
+
+-> Since the network was just one big connected "island" to start with, it did not change a thing.
+
+- then, all characters with more than 10 relations where hidden
+
+-> this hides nodes which were connecting with many others, so that we end up with many groups, disconnected from each others.
+
+//ST: !
+
+Now instead, placing the filter degree *inside* the filter on giant component:
+
+image::en/filter-order-2-en.png[align="center",title="Same filters in another configuration"]
+{nbsp} +
+
+//ST: !
+In this second case,
+
+- starting from the complete network, all characters with more than 10 relations where deleted.
+
+-> this created a network made of many disconnected groups of nodes
+
+- then the giant component filter is applied,
+
+-> which had for effect to hide small groups, to keep in view only the biggest group of connected nodes.
+
+//ST: !
+
+WARNING: In summary: be careful how you apply several filters at once, this might have an effect on the logic of filtering.
+
+== filter operators
+//ST: Filter operators
+
+//ST: !
+==== 1. The MASK operator
+
+//ST: !
+Imagine you are interested in the female characters of the novel "Les Miserables".
+
+- you are interested in female characters and the relations among them
+- you are interested in the relations between female characters and male characters
+- you are *not* interested in the relations between male characters
+
+How to display this?
+
+//ST: !
+The MASK operator applied on the gender partition filter enables you to:
+
+- show all characters
+- relations between female characters
+- _and relations between male and female characters_
+- _but masking male-male relations_
+
+//ST: !
+
+image::en/operator-mask-1-en.png[align="center",title="Using the MASK operator"]
+{nbsp} +
+
+//ST: !
+It is also possible to hide / show only some of the directed relations between the visible graph and the filtered out graph:
+
+image::en/operator-mask-2-en.png[align="center",title="Parameters of the MASK operator"]
+{nbsp} +
+
+//ST: !
+==== 2. The UNION operator
+
+//ST: !
+
+Imagine you are interested in the characters with names starting with "L" or "J" in "Les Miserables".
+
+How to display only these characters?
+
+//ST: !
+We will need to apply filters on the `Label` of the nodes, which contains the names of the characters.
+
+However, looking at the "catalogue" of filters, we see no filter on `Label`. The reason is that `Label` is an internal property of nodes, inaccessible to filters.
+
+So we must first copy the Labels of the nodes in a new attribute, which we will be able to apply a filter on.
+
+Let's switch to the data laboratory and add this attribute:
+
+//ST: !
+
+image::Adding-a-column-for-Names.png[align="center",title="Adding a column for Names"]
+{nbsp} +
+
+
+//ST: !
+
+image::Copying-to-this-new-column.png[align="center",title="Copying to this new column"]
+{nbsp} +
+
+//ST: !
+We now have an attribute called "Name" that we can find in the Filters:
+
+image::New-filter-available.png[align="center",title="New filter available"]
+{nbsp} +
+
+//ST: !
+This is how the filter on Name and its parameters look like in the zone:
+
+image::en/filter-name-1-en.png[align="center",title="Name Filter"]
+{nbsp} +
+
+//ST: !
+To recall, we want to show only the characters which name start with "L" or "J". Let's start with the "L" characters.
+
+We need to find the names which match the pattern *`Start with an L`*. The way to describe a pattern in text is called a "regular expression".
+
+Said differently, _a regular expressions (also called "regex") is a convenient way to express a pattern we search for in a text_.
+
+//ST: !
+Regular expressions can become very sophisticated. But here, we need just a simple one:
+
+[source,regex]
+L.*
+
+Let's examine what the L, the dot and the star mean.
+
+//ST: !
+
+- the letter "L" means we want names starting with this first letter
+- . the dot means: any character
+- * the star means: the previous character, repeated any time.
+
+So: "select nodes which have a name starting with L, followed by any character, in any number"
+
+//ST: !
+Please note that you need to check the box "regex":
+
+image::en/filter-name-2-en.png[align="center",title="Using a regular expression in a filter"]
+{nbsp} +
+
+//ST: !
+When the filter is applied, only the characters wit a name starting with L will be displayed:
+
+image::en/filter-name-3-en.png[align="center",title="Using a regular expression in a filter"]
+{nbsp} +
+
+//ST: !
+How to filter characters with a name starting with the letter "L" or "J"?
+
+We could rely on a more complex regular expression to do this:
+
+[source,regex]
+[LJ].*
+
+Meaning: "select nodes which have a name starting with L or J, followed by any characters"
+
+//ST: !
+But we can also rely on 2 filters: one for L, one for J. Nesting one inside another would not work, it would mean:
+
+"show nodes which start with an L, and among them, only those which start with a J"
+
+-> no node can meet this condition, so they would all be invisible.
+
+//ST: !
+Instead, we should use the *`UNION`* operator that can be found here:
+
+image::en/filter-operator-union-1-en.png[align="center",title="The UNION operator in filters"]
+{nbsp} +
+
+//ST: !
+Drag it to the zone, and then drag inside it twice the `Attributes -> Equal -> Name` filter:
+
+image::en/filter-operator-union-2-en.png[align="center",title="The UNION operator and 2 subfilters"]
+{nbsp} +
+
+//ST: !
+In the settings of the first Name filter, put the regular expression:
+[source,regex]
+L.*
+
+In the second Name filter, put:
+[source,regex]
+J.*
+
+(make sure the "regex" box is checked in both cases)
+
+//ST: !
+As a result, the nodes selected by both filters are added up in the display:
+
+image::en/filter-operator-union-3-en.png[align="center",title="The UNION operator and 2 subfilters"]
+{nbsp} +
+
+//ST: !
+==== 3. The NOT operator
+
+//ST: !
+The NOT operator flips the result of a filter: what was hidden becomes visible and vice and versa.
+
+//ST: !
+Example: if we want to display all characters except for those returned by a UNION on 2 Name filters on L and J initials:
+
+image::en/filter-operator-not-3-en.png[align="center",title="The NOT nodes operator - 1"]
+{nbsp} +
+
+//ST: !
+Same effect, but applying the NOT operator on single filter using a regex on L or J:
+
+image::en/filter-operator-not-1-en.png[align="center",title="The NOT nodes operator - 2"]
+{nbsp} +
+
+//ST: !
+Same effect again, achieved without using the NOT operator. In regular expressions the ^ sign inside square brackets means "NOT":
+
+[source,regex]
+[^LJ].*
+
+image::en/filter-operator-not-2-en.png[align="center",title="Achieving a NOT effect with regex"]
+{nbsp} +
+
+//ST: !
+Tutorials about regular expressions:
+
+- http://www.regular-expressions.info/quickstart.html[https://regexone.com/]
+- http://www.themacroscope.org/?page_id=643[http://www.themacroscope.org/?page_id=643]
+
+And a web page where you can test your regular expressions: http://regexpal.com[http://regexpal.com]
+
+== more tutorials on using filters in Gephi
+//ST: More tutorials on using filters in Gephi
+
+//ST: !
+
+- https://www.youtube.com/watch?v=UrrWA_t1rjc[Video on using filters by Jen Golbeck]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/using-filters-en_temp_pdf.md b/docs/generated-pdf/subdir/using-filters-en_temp_pdf.md
new file mode 100644
index 0000000..1e54639
--- /dev/null
+++ b/docs/generated-pdf/subdir/using-filters-en_temp_pdf.md
@@ -0,0 +1,438 @@
+= Using filters
+Clément Levallois
+2017-02-10
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== download a network file for practice
+//ST: download a network file for practice
+
+//ST: !
+
+link:../resources/miserables.zip[download this zip file] and unzip it on your computer.
+
+or use this direct link: https://tinyurl.com/gephi-tuto-3[https://tinyurl.com/gephi-tuto-3]
+
+You should find the file `miserables.gexf` in it. Save it in a folder you will remember (or create a folder specially for this small project).
+
+//ST: description of the file / the network
+//ST: !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+
+== open the network in Gephi
+//ST: open the network in Gephi
+
+//ST: !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables.gexf` on your computer and open it
+
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+{nbsp} +
+
+//ST: !
+A report window will open, giving you basic info on the network you opened:
+
+image::en/opening-file-report-en.png[align="center", title="report window"]
+{nbsp} +
+
+//ST: !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+//ST: !
+image::result_miserables.png[align="center",title="The network we will use"]
+{nbsp} +
+
+== getting a sense of the attributes in the data laboratory
+//ST: getting a sense of the attributes in the data laboratory
+
+//ST: !
+We can switch to the data laboratory to see the underlying data:
+
+image::Switching-to-the-data-laboratory.png[align="center",title="Switching to the data laboratory"]
+{nbsp} +
+
+
+//ST: !
+We see that the nodes of the network have many attributes. In particular, each have a Gender and a measure of how central they are:
+
+image::Nodes-attributes..png[align="center",title="Nodes attributes."]
+{nbsp} +
+
+//ST: !
+This is the list of edges (relations) in the network. Notice that they have a "weight" (a "strength").
+
+image::Edges-attributes..png[align="center",title="Edges attributes".]
+{nbsp} +
+
+== discovering the filter panel
+//ST: discovering the filter panel
+
+//ST: !
+In the overview, make sure the Filter panel is displayed:
+
+image::Making-the-Filter-panel-visible..png[align="center",title="Making the Filter panel visible."]
+{nbsp} +
+
+
+//ST: !
+How the Filter panel works:
+
+image::Workflow-of-filters--size=stretch.png[align="center",title="Workflow of filters", size="stretch"]
+{nbsp} +
+
+
+//ST: !
+An example: hiding edges with weight lower than 2
+
+
+{nbsp} +
+
+image::en/filter-edge-weight-1-en.png[align="center",title="Filtering out edges with weight lower than 2."]
+
+{github-root}images/en/filter-edge-weight-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-2
+
+
+//ST: !
+When you are finished using a filter in the zone, right click on it and select "remove".
+
+== combining 2 filters
+//ST: combining 2 filters
+
+//ST: !
+One filter is applied AFTER this other:
+
+*The first filter to be applied is NESTED (placed inside) the second one as a "subfilter"*
+
+Which filter should be placed inside which? Let's look at different examples:
+
+//ST: !
+==== 1. Case when the placement of filters makes no difference
+
+//ST: !
+Goal: Keeping on screen only the female characters which have a tie (an edge, a relation) of at least strength 2.
+
+-> place the filter "edge weight" inside the filter "Gender":
+
+//ST: !
+
+image::Filter-on-the-Gender-attribute.png[align="center",title="Filter on the Gender attribute"]
+{nbsp} +
+
+//ST: !
+
+image::Filter-on-edge-weight.png[align="center",title="Filter on edge weight"]
+{nbsp} +
+
+//ST: !
+
+
+{nbsp} +
+
+image::en/filter-edge-weight-gender-partition1-en.png[align="center",title="Keeping only female characters with at least 2 ties"]
+
+{github-root}images/en/filter-edge-weight-gender-partition-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-1
+
+//ST: !
+In this case, it was equivalent to:
+
+- nest the "Gender" filter inside the "Edge weight" filter
+or
+- nest the "Edge weight" filter inside the "Gender" Filter
+
+-> The result was the same (the network on screen is identical in both cases)
+
+//ST: !
+==== 2. Case when the placement of filters makes a difference
+
+//ST: !
+Here, we want to visualize:
+
+- only the nodes which have *less than* 10 relations <1>
+- and among these, only those which form the "main island" of the network (we want to hide small detached groups of nodes) <2>
+
+<1> in technical terms, nodes with a `degree` of less than 10.
+<2> in technical terms, we are looking for the `giant component`
+
+//ST: !
+
+image::en/filter-degree-range-1-en.png[align="center",title="Filter on degree"]
+{nbsp} +
+
+//ST: !
+
+image::en/filter-giant-component-1-en.png[align="center",title="Filter on giant component"]
+{nbsp} +
+
+//ST: !
+We will see that the placement on the filters in the zone will make a difference.
+
+First, let us place the filter on giant component *inside* the filter on degree:
+
+//ST: !
+
+image::en/filter-order-1-en.png[align="center",title="Filters in one configuration"]
+{nbsp} +
+
+//ST: !
+In this first case,
+
+- only the giant component of the network was made visible.
+
+-> Since the network was just one big connected "island" to start with, it did not change a thing.
+
+- then, all characters with more than 10 relations where hidden
+
+-> this hides nodes which were connecting with many others, so that we end up with many groups, disconnected from each others.
+
+//ST: !
+
+Now instead, placing the filter degree *inside* the filter on giant component:
+
+image::en/filter-order-2-en.png[align="center",title="Same filters in another configuration"]
+{nbsp} +
+
+//ST: !
+In this second case,
+
+- starting from the complete network, all characters with more than 10 relations where deleted.
+
+-> this created a network made of many disconnected groups of nodes
+
+- then the giant component filter is applied,
+
+-> which had for effect to hide small groups, to keep in view only the biggest group of connected nodes.
+
+//ST: !
+
+WARNING: In summary: be careful how you apply several filters at once, this might have an effect on the logic of filtering.
+
+== filter operators
+//ST: Filter operators
+
+//ST: !
+==== 1. The MASK operator
+
+//ST: !
+Imagine you are interested in the female characters of the novel "Les Miserables".
+
+- you are interested in female characters and the relations among them
+- you are interested in the relations between female characters and male characters
+- you are *not* interested in the relations between male characters
+
+How to display this?
+
+//ST: !
+The MASK operator applied on the gender partition filter enables you to:
+
+- show all characters
+- relations between female characters
+- _and relations between male and female characters_
+- _but masking male-male relations_
+
+//ST: !
+
+image::en/operator-mask-1-en.png[align="center",title="Using the MASK operator"]
+{nbsp} +
+
+//ST: !
+It is also possible to hide / show only some of the directed relations between the visible graph and the filtered out graph:
+
+image::en/operator-mask-2-en.png[align="center",title="Parameters of the MASK operator"]
+{nbsp} +
+
+//ST: !
+==== 2. The UNION operator
+
+//ST: !
+
+Imagine you are interested in the characters with names starting with "L" or "J" in "Les Miserables".
+
+How to display only these characters?
+
+//ST: !
+We will need to apply filters on the `Label` of the nodes, which contains the names of the characters.
+
+However, looking at the "catalogue" of filters, we see no filter on `Label`. The reason is that `Label` is an internal property of nodes, inaccessible to filters.
+
+So we must first copy the Labels of the nodes in a new attribute, which we will be able to apply a filter on.
+
+Let's switch to the data laboratory and add this attribute:
+
+//ST: !
+
+image::Adding-a-column-for-Names.png[align="center",title="Adding a column for Names"]
+{nbsp} +
+
+
+//ST: !
+
+image::Copying-to-this-new-column.png[align="center",title="Copying to this new column"]
+{nbsp} +
+
+//ST: !
+We now have an attribute called "Name" that we can find in the Filters:
+
+image::New-filter-available.png[align="center",title="New filter available"]
+{nbsp} +
+
+//ST: !
+This is how the filter on Name and its parameters look like in the zone:
+
+image::en/filter-name-1-en.png[align="center",title="Name Filter"]
+{nbsp} +
+
+//ST: !
+To recall, we want to show only the characters which name start with "L" or "J". Let's start with the "L" characters.
+
+We need to find the names which match the pattern *`Start with an L`*. The way to describe a pattern in text is called a "regular expression".
+
+Said differently, _a regular expressions (also called "regex") is a convenient way to express a pattern we search for in a text_.
+
+//ST: !
+Regular expressions can become very sophisticated. But here, we need just a simple one:
+
+[source,regex]
+L.*
+
+Let's examine what the L, the dot and the star mean.
+
+//ST: !
+
+- the letter "L" means we want names starting with this first letter
+- . the dot means: any character
+- * the star means: the previous character, repeated any time.
+
+So: "select nodes which have a name starting with L, followed by any character, in any number"
+
+//ST: !
+Please note that you need to check the box "regex":
+
+image::en/filter-name-2-en.png[align="center",title="Using a regular expression in a filter"]
+{nbsp} +
+
+//ST: !
+When the filter is applied, only the characters wit a name starting with L will be displayed:
+
+image::en/filter-name-3-en.png[align="center",title="Using a regular expression in a filter"]
+{nbsp} +
+
+//ST: !
+How to filter characters with a name starting with the letter "L" or "J"?
+
+We could rely on a more complex regular expression to do this:
+
+[source,regex]
+[LJ].*
+
+Meaning: "select nodes which have a name starting with L or J, followed by any characters"
+
+//ST: !
+But we can also rely on 2 filters: one for L, one for J. Nesting one inside another would not work, it would mean:
+
+"show nodes which start with an L, and among them, only those which start with a J"
+
+-> no node can meet this condition, so they would all be invisible.
+
+//ST: !
+Instead, we should use the *`UNION`* operator that can be found here:
+
+image::en/filter-operator-union-1-en.png[align="center",title="The UNION operator in filters"]
+{nbsp} +
+
+//ST: !
+Drag it to the zone, and then drag inside it twice the `Attributes -> Equal -> Name` filter:
+
+image::en/filter-operator-union-2-en.png[align="center",title="The UNION operator and 2 subfilters"]
+{nbsp} +
+
+//ST: !
+In the settings of the first Name filter, put the regular expression:
+[source,regex]
+L.*
+
+In the second Name filter, put:
+[source,regex]
+J.*
+
+(make sure the "regex" box is checked in both cases)
+
+//ST: !
+As a result, the nodes selected by both filters are added up in the display:
+
+image::en/filter-operator-union-3-en.png[align="center",title="The UNION operator and 2 subfilters"]
+{nbsp} +
+
+//ST: !
+==== 3. The NOT operator
+
+//ST: !
+The NOT operator flips the result of a filter: what was hidden becomes visible and vice and versa.
+
+//ST: !
+Example: if we want to display all characters except for those returned by a UNION on 2 Name filters on L and J initials:
+
+image::en/filter-operator-not-3-en.png[align="center",title="The NOT nodes operator - 1"]
+{nbsp} +
+
+//ST: !
+Same effect, but applying the NOT operator on single filter using a regex on L or J:
+
+image::en/filter-operator-not-1-en.png[align="center",title="The NOT nodes operator - 2"]
+{nbsp} +
+
+//ST: !
+Same effect again, achieved without using the NOT operator. In regular expressions the ^ sign inside square brackets means "NOT":
+
+[source,regex]
+[^LJ].*
+
+image::en/filter-operator-not-2-en.png[align="center",title="Achieving a NOT effect with regex"]
+{nbsp} +
+
+//ST: !
+Tutorials about regular expressions:
+
+- http://www.regular-expressions.info/quickstart.html[https://regexone.com/]
+- http://www.themacroscope.org/?page_id=643[http://www.themacroscope.org/?page_id=643]
+
+And a web page where you can test your regular expressions: http://regexpal.com[http://regexpal.com]
+
+== more tutorials on using filters in Gephi
+//ST: More tutorials on using filters in Gephi
+
+//ST: !
+
+- https://www.youtube.com/watch?v=UrrWA_t1rjc[Video on using filters by Jen Golbeck]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/using-filters-en_temp_slides.md b/docs/generated-pdf/subdir/using-filters-en_temp_slides.md
new file mode 100644
index 0000000..8abea3c
--- /dev/null
+++ b/docs/generated-pdf/subdir/using-filters-en_temp_slides.md
@@ -0,0 +1,477 @@
+= Using filters
+Clément Levallois
+2017-02-10
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:github-root: https://github.com/seinecle/gephi-tutorials/blob/master/src/main/asciidoc/
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== download a network file for practice
+
+== !
+
+link:../resources/miserables.zip[download this zip file] and unzip it on your computer.
+
+or use this direct link: https://tinyurl.com/gephi-tuto-3[https://tinyurl.com/gephi-tuto-3]
+
+You should find the file `miserables.gexf` in it. Save it in a folder you will remember (or create a folder specially for this small project).
+
+== description of the file / the network
+== !
+
+This file contains a network representing "who appears next to whom" in the 19th century novel _Les Misérables_ by Victor Hugofootnote:[D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993)].
+
+A link between characters A and B means they appeared on the same page or paragraph in the novel.
+
+The file name ends with ".gexf", which just means this is a text file where the network information is stored (name of the characters, their relations, etc.), following some conventions.
+
+
+== open the network in Gephi
+
+== !
+- open Gephi. On the Welcome screen that appears, click on `Open Graph File`
+- find `miserables.gexf` on your computer and open it
+
+[.stretch]
+image::en/gephi-welcome-screen-open-graph-en.png[align="center", title="welcome screen"]
+
+
+== !
+A report window will open, giving you basic info on the network you opened:
+
+[.stretch]
+image::en/opening-file-report-en.png[align="center", title="report window"]
+
+
+== !
+This tells you that the network comprises 74 characters, connected by 248 links.
+
+Links are undirected, meaning that if A is connected to B, then it is the same as B connected to A.
+
+The report also tells us the graph is not dynamic: it means there is no evolution or chronology, it won't "move in time".
+
+Click on `OK` to see the graph in Gephi.
+
+== !
+[.stretch]
+image::result_miserables.png[align="center",title="The network we will use"]
+
+
+== getting a sense of the attributes in the data laboratory
+
+== !
+We can switch to the data laboratory to see the underlying data:
+
+[.stretch]
+image::Switching-to-the-data-laboratory.png[align="center",title="Switching to the data laboratory"]
+
+
+
+== !
+We see that the nodes of the network have many attributes. In particular, each have a Gender and a measure of how central they are:
+
+[.stretch]
+image::Nodes-attributes..png[align="center",title="Nodes attributes."]
+
+
+== !
+This is the list of edges (relations) in the network. Notice that they have a "weight" (a "strength").
+
+[.stretch]
+image::Edges-attributes..png[align="center",title="Edges attributes".]
+
+
+== discovering the filter panel
+
+== !
+In the overview, make sure the Filter panel is displayed:
+
+[.stretch]
+image::Making-the-Filter-panel-visible..png[align="center",title="Making the Filter panel visible."]
+
+
+
+== !
+How the Filter panel works:
+
+[.stretch]
+image::Workflow-of-filters--size=stretch.png[align="center",title="Workflow of filters", size="stretch"]
+
+
+
+== !
+An example: hiding edges with weight lower than 2
+
+[.stretch]
+image::en/filter-edge-weight-en.gif[align="center",title="How to use filters."]
+
+
+//PDF: image::en/filter-edge-weight-1-en.png[align="center",title="Filtering out edges with weight lower than 2."]
+
+//PDF: {github-root}images/en/filter-edge-weight-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-2
+
+
+== !
+When you are finished using a filter in the zone, right click on it and select "remove".
+
+== combining 2 filters
+
+== !
+One filter is applied AFTER this other:
+
+*The first filter to be applied is NESTED (placed inside) the second one as a "subfilter"*
+
+Which filter should be placed inside which? Let's look at different examples:
+
+== !
+==== 1. Case when the placement of filters makes no difference
+
+== !
+Goal: Keeping on screen only the female characters which have a tie (an edge, a relation) of at least strength 2.
+
+-> place the filter "edge weight" inside the filter "Gender":
+
+== !
+
+[.stretch]
+image::Filter-on-the-Gender-attribute.png[align="center",title="Filter on the Gender attribute"]
+
+
+== !
+
+[.stretch]
+image::Filter-on-edge-weight.png[align="center",title="Filter on edge weight"]
+
+
+== !
+
+[.stretch]
+image::en/filter-edge-weight-gender-partition-en.gif[align="center",title="Keeping only female characters with at least 2 ties"]
+
+
+//PDF: image::en/filter-edge-weight-gender-partition1-en.png[align="center",title="Keeping only female characters with at least 2 ties"]
+
+//PDF: {github-root}images/en/filter-edge-weight-gender-partition-en.gif[view online animation] - link: https://tinyurl.com/gephi-tuto-1
+
+== !
+In this case, it was equivalent to:
+
+- nest the "Gender" filter inside the "Edge weight" filter
+or
+- nest the "Edge weight" filter inside the "Gender" Filter
+
+-> The result was the same (the network on screen is identical in both cases)
+
+== !
+==== 2. Case when the placement of filters makes a difference
+
+== !
+Here, we want to visualize:
+
+- only the nodes which have *less than* 10 relations <1>
+- and among these, only those which form the "main island" of the network (we want to hide small detached groups of nodes) <2>
+
+<1> in technical terms, nodes with a `degree` of less than 10.
+<2> in technical terms, we are looking for the `giant component`
+
+== !
+
+[.stretch]
+image::en/filter-degree-range-1-en.png[align="center",title="Filter on degree"]
+
+
+== !
+
+[.stretch]
+image::en/filter-giant-component-1-en.png[align="center",title="Filter on giant component"]
+
+
+== !
+We will see that the placement on the filters in the zone will make a difference.
+
+First, let us place the filter on giant component *inside* the filter on degree:
+
+== !
+
+[.stretch]
+image::en/filter-order-1-en.png[align="center",title="Filters in one configuration"]
+
+
+== !
+In this first case,
+
+- only the giant component of the network was made visible.
+
+-> Since the network was just one big connected "island" to start with, it did not change a thing.
+
+- then, all characters with more than 10 relations where hidden
+
+-> this hides nodes which were connecting with many others, so that we end up with many groups, disconnected from each others.
+
+== !
+
+Now instead, placing the filter degree *inside* the filter on giant component:
+
+[.stretch]
+image::en/filter-order-2-en.png[align="center",title="Same filters in another configuration"]
+
+
+== !
+In this second case,
+
+- starting from the complete network, all characters with more than 10 relations where deleted.
+
+-> this created a network made of many disconnected groups of nodes
+
+- then the giant component filter is applied,
+
+-> which had for effect to hide small groups, to keep in view only the biggest group of connected nodes.
+
+== !
+
+WARNING: In summary: be careful how you apply several filters at once, this might have an effect on the logic of filtering.
+
+== Filter operators
+
+== !
+==== 1. The MASK operator
+
+== !
+Imagine you are interested in the female characters of the novel "Les Miserables".
+
+- you are interested in female characters and the relations among them
+- you are interested in the relations between female characters and male characters
+- you are *not* interested in the relations between male characters
+
+How to display this?
+
+== !
+The MASK operator applied on the gender partition filter enables you to:
+
+- show all characters
+- relations between female characters
+- _and relations between male and female characters_
+- _but masking male-male relations_
+
+== !
+
+[.stretch]
+image::en/operator-mask-1-en.png[align="center",title="Using the MASK operator"]
+
+
+== !
+It is also possible to hide / show only some of the directed relations between the visible graph and the filtered out graph:
+
+[.stretch]
+image::en/operator-mask-2-en.png[align="center",title="Parameters of the MASK operator"]
+
+
+== !
+==== 2. The UNION operator
+
+== !
+
+Imagine you are interested in the characters with names starting with "L" or "J" in "Les Miserables".
+
+How to display only these characters?
+
+== !
+We will need to apply filters on the `Label` of the nodes, which contains the names of the characters.
+
+However, looking at the "catalogue" of filters, we see no filter on `Label`. The reason is that `Label` is an internal property of nodes, inaccessible to filters.
+
+So we must first copy the Labels of the nodes in a new attribute, which we will be able to apply a filter on.
+
+Let's switch to the data laboratory and add this attribute:
+
+== !
+
+[.stretch]
+image::Adding-a-column-for-Names.png[align="center",title="Adding a column for Names"]
+
+
+
+== !
+
+[.stretch]
+image::Copying-to-this-new-column.png[align="center",title="Copying to this new column"]
+
+
+== !
+We now have an attribute called "Name" that we can find in the Filters:
+
+[.stretch]
+image::New-filter-available.png[align="center",title="New filter available"]
+
+
+== !
+This is how the filter on Name and its parameters look like in the zone:
+
+[.stretch]
+image::en/filter-name-1-en.png[align="center",title="Name Filter"]
+
+
+== !
+To recall, we want to show only the characters which name start with "L" or "J". Let's start with the "L" characters.
+
+We need to find the names which match the pattern *`Start with an L`*. The way to describe a pattern in text is called a "regular expression".
+
+Said differently, _a regular expressions (also called "regex") is a convenient way to express a pattern we search for in a text_.
+
+== !
+Regular expressions can become very sophisticated. But here, we need just a simple one:
+
+[source,regex]
+L.*
+
+Let's examine what the L, the dot and the star mean.
+
+== !
+
+- the letter "L" means we want names starting with this first letter
+- . the dot means: any character
+- * the star means: the previous character, repeated any time.
+
+So: "select nodes which have a name starting with L, followed by any character, in any number"
+
+== !
+Please note that you need to check the box "regex":
+
+[.stretch]
+image::en/filter-name-2-en.png[align="center",title="Using a regular expression in a filter"]
+
+
+== !
+When the filter is applied, only the characters wit a name starting with L will be displayed:
+
+[.stretch]
+image::en/filter-name-3-en.png[align="center",title="Using a regular expression in a filter"]
+
+
+== !
+How to filter characters with a name starting with the letter "L" or "J"?
+
+We could rely on a more complex regular expression to do this:
+
+[source,regex]
+[LJ].*
+
+Meaning: "select nodes which have a name starting with L or J, followed by any characters"
+
+== !
+But we can also rely on 2 filters: one for L, one for J. Nesting one inside another would not work, it would mean:
+
+"show nodes which start with an L, and among them, only those which start with a J"
+
+-> no node can meet this condition, so they would all be invisible.
+
+== !
+Instead, we should use the *`UNION`* operator that can be found here:
+
+[.stretch]
+image::en/filter-operator-union-1-en.png[align="center",title="The UNION operator in filters"]
+
+
+== !
+Drag it to the zone, and then drag inside it twice the `Attributes -> Equal -> Name` filter:
+
+[.stretch]
+image::en/filter-operator-union-2-en.png[align="center",title="The UNION operator and 2 subfilters"]
+
+
+== !
+In the settings of the first Name filter, put the regular expression:
+[source,regex]
+L.*
+
+In the second Name filter, put:
+[source,regex]
+J.*
+
+(make sure the "regex" box is checked in both cases)
+
+== !
+As a result, the nodes selected by both filters are added up in the display:
+
+[.stretch]
+image::en/filter-operator-union-3-en.png[align="center",title="The UNION operator and 2 subfilters"]
+
+
+== !
+==== 3. The NOT operator
+
+== !
+The NOT operator flips the result of a filter: what was hidden becomes visible and vice and versa.
+
+== !
+Example: if we want to display all characters except for those returned by a UNION on 2 Name filters on L and J initials:
+
+[.stretch]
+image::en/filter-operator-not-3-en.png[align="center",title="The NOT nodes operator - 1"]
+
+
+== !
+Same effect, but applying the NOT operator on single filter using a regex on L or J:
+
+[.stretch]
+image::en/filter-operator-not-1-en.png[align="center",title="The NOT nodes operator - 2"]
+
+
+== !
+Same effect again, achieved without using the NOT operator. In regular expressions the ^ sign inside square brackets means "NOT":
+
+[source,regex]
+[^LJ].*
+
+[.stretch]
+image::en/filter-operator-not-2-en.png[align="center",title="Achieving a NOT effect with regex"]
+
+
+== !
+Tutorials about regular expressions:
+
+- http://www.regular-expressions.info/quickstart.html[https://regexone.com/]
+- http://www.themacroscope.org/?page_id=643[http://www.themacroscope.org/?page_id=643]
+
+And a web page where you can test your regular expressions: http://regexpal.com[http://regexpal.com]
+
+== More tutorials on using filters in Gephi
+
+== !
+
+- https://www.youtube.com/watch?v=UrrWA_t1rjc[Video on using filters by Jen Golbeck]
+
+
+== The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/what-is-gephi-en_temp_common.md b/docs/generated-pdf/subdir/what-is-gephi-en_temp_common.md
new file mode 100644
index 0000000..0780cd8
--- /dev/null
+++ b/docs/generated-pdf/subdir/what-is-gephi-en_temp_common.md
@@ -0,0 +1,70 @@
+= What is Gephi?
+Clément Levallois
+2017-01-20
+
+last modified: {docdate}
+
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== the leading software to visualize networks on Linux, Windows and Apple computers
+
+//ST: the leading desktop software to visualize and explore networks
+- more than 2 million downloads,
+- used by analysts, academics, journalists,
+- stable on Windows, Mac and Linux,
+- in continuous development
+
+
+== open source & free (including for commercial purposes)
+
+//ST: open source & free (including for commercial purposes)
+
+Gephi adopts a licencing model which makes it free to use, sell, or include the source code of Gephi in larger applications.
+
+Developers who modify the Gephi code base are required to make these changes public, to benefit the community.
+
+
+== "Photoshop^TM^ for graphs"
+
+//ST: "Photoshop^TM^ for graphs"
+
+- for networks of all types (social, semantic, biological, etc.)
+- attributes and properties of the network are made visible: not just the http://www.webopedia.com/quick_ref/topologies.asp[topology] of the network.
+- Gephi provides functions to explore the network: pan, filter, zoom, colorize, label, etc.
+- measures and metrics can be computed, and then rendered visually.
+
+
+== committed to quality and performance
+
+//ST: committed to quality and performance
+
+- Gephi won a http://www.oracle.com/technetwork/articles/java/dukeschoicewinners-171159.html[Duke Award in 2010]. Duke awards "recognize and honor extreme innovation in the world of Java technology".
+- Gephi evolved importantly since 2006 to adopt new, best performing techs for the visualization of very large graphs on the desktop
+- The code base of Gephi is maintained with the best standards to insure a https://gephi.wordpress.com/tag/code/[a stable application, ease of maintainance and modularity / extensibility].
+
+
+== modular and extensible
+
+//ST: modular and extensible
+
+- Gephi is "cut" in a large number of modules, each providing a specific function. This makes it easier to maintain and evolve the software, by working on independent parts.
+- Since version 0.9 the https://gephi.wordpress.com/2015/12/04/gephi-boosts-its-performance-with-new-graphstore-core/["graph store"] of Gephi has become a completely independent module: it can be reused by another software.
+- Anybody can create new modules for Gephi! https://gephi.org/plugins/#/[They are called "plugins"]. They integrate with Gephi seamlessly.
+
+== created, maintained and documented by volunteers, for the community.
+
+//ST: created, maintained and documented by volunteers, for the community.
+
+- Gephi itself is open source and free
+- The https://consortium.gephi.org/[Gephi Consortium] was created to ensure Gephi will remain so
+- The https://github.com/gephi/gephi/graphs/contributors[original contributors and their followers] are volunteers
+- The support to users through http://forum-gephi.org/[forums], https://www.facebook.com/groups/gephi/[discussion groups] and https://gephi.org/users/[documents] is provided for free.
diff --git a/docs/generated-pdf/subdir/what-is-gephi-en_temp_html.md b/docs/generated-pdf/subdir/what-is-gephi-en_temp_html.md
new file mode 100644
index 0000000..8f2c926
--- /dev/null
+++ b/docs/generated-pdf/subdir/what-is-gephi-en_temp_html.md
@@ -0,0 +1,87 @@
+= What is Gephi?
+Clément Levallois
+2017-01-20
+
+last modified: {docdate}
+
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== the leading software to visualize networks on Linux, Windows and Apple computers
+
+//ST: the leading desktop software to visualize and explore networks
+- more than 2 million downloads,
+- used by analysts, academics, journalists,
+- stable on Windows, Mac and Linux,
+- in continuous development
+
+
+== open source & free (including for commercial purposes)
+
+//ST: open source & free (including for commercial purposes)
+
+Gephi adopts a licencing model which makes it free to use, sell, or include the source code of Gephi in larger applications.
+
+Developers who modify the Gephi code base are required to make these changes public, to benefit the community.
+
+
+== "Photoshop^TM^ for graphs"
+
+//ST: "Photoshop^TM^ for graphs"
+
+- for networks of all types (social, semantic, biological, etc.)
+- attributes and properties of the network are made visible: not just the http://www.webopedia.com/quick_ref/topologies.asp[topology] of the network.
+- Gephi provides functions to explore the network: pan, filter, zoom, colorize, label, etc.
+- measures and metrics can be computed, and then rendered visually.
+
+
+== committed to quality and performance
+
+//ST: committed to quality and performance
+
+- Gephi won a http://www.oracle.com/technetwork/articles/java/dukeschoicewinners-171159.html[Duke Award in 2010]. Duke awards "recognize and honor extreme innovation in the world of Java technology".
+- Gephi evolved importantly since 2006 to adopt new, best performing techs for the visualization of very large graphs on the desktop
+- The code base of Gephi is maintained with the best standards to insure a https://gephi.wordpress.com/tag/code/[a stable application, ease of maintainance and modularity / extensibility].
+
+
+== modular and extensible
+
+//ST: modular and extensible
+
+- Gephi is "cut" in a large number of modules, each providing a specific function. This makes it easier to maintain and evolve the software, by working on independent parts.
+- Since version 0.9 the https://gephi.wordpress.com/2015/12/04/gephi-boosts-its-performance-with-new-graphstore-core/["graph store"] of Gephi has become a completely independent module: it can be reused by another software.
+- Anybody can create new modules for Gephi! https://gephi.org/plugins/#/[They are called "plugins"]. They integrate with Gephi seamlessly.
+
+== created, maintained and documented by volunteers, for the community.
+
+//ST: created, maintained and documented by volunteers, for the community.
+
+- Gephi itself is open source and free
+- The https://consortium.gephi.org/[Gephi Consortium] was created to ensure Gephi will remain so
+- The https://github.com/gephi/gephi/graphs/contributors[original contributors and their followers] are volunteers
+- The support to users through http://forum-gephi.org/[forums], https://www.facebook.com/groups/gephi/[discussion groups] and https://gephi.org/users/[documents] is provided for free.
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/what-is-gephi-en_temp_pdf.md b/docs/generated-pdf/subdir/what-is-gephi-en_temp_pdf.md
new file mode 100644
index 0000000..0780cd8
--- /dev/null
+++ b/docs/generated-pdf/subdir/what-is-gephi-en_temp_pdf.md
@@ -0,0 +1,70 @@
+= What is Gephi?
+Clément Levallois
+2017-01-20
+
+last modified: {docdate}
+
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== the leading software to visualize networks on Linux, Windows and Apple computers
+
+//ST: the leading desktop software to visualize and explore networks
+- more than 2 million downloads,
+- used by analysts, academics, journalists,
+- stable on Windows, Mac and Linux,
+- in continuous development
+
+
+== open source & free (including for commercial purposes)
+
+//ST: open source & free (including for commercial purposes)
+
+Gephi adopts a licencing model which makes it free to use, sell, or include the source code of Gephi in larger applications.
+
+Developers who modify the Gephi code base are required to make these changes public, to benefit the community.
+
+
+== "Photoshop^TM^ for graphs"
+
+//ST: "Photoshop^TM^ for graphs"
+
+- for networks of all types (social, semantic, biological, etc.)
+- attributes and properties of the network are made visible: not just the http://www.webopedia.com/quick_ref/topologies.asp[topology] of the network.
+- Gephi provides functions to explore the network: pan, filter, zoom, colorize, label, etc.
+- measures and metrics can be computed, and then rendered visually.
+
+
+== committed to quality and performance
+
+//ST: committed to quality and performance
+
+- Gephi won a http://www.oracle.com/technetwork/articles/java/dukeschoicewinners-171159.html[Duke Award in 2010]. Duke awards "recognize and honor extreme innovation in the world of Java technology".
+- Gephi evolved importantly since 2006 to adopt new, best performing techs for the visualization of very large graphs on the desktop
+- The code base of Gephi is maintained with the best standards to insure a https://gephi.wordpress.com/tag/code/[a stable application, ease of maintainance and modularity / extensibility].
+
+
+== modular and extensible
+
+//ST: modular and extensible
+
+- Gephi is "cut" in a large number of modules, each providing a specific function. This makes it easier to maintain and evolve the software, by working on independent parts.
+- Since version 0.9 the https://gephi.wordpress.com/2015/12/04/gephi-boosts-its-performance-with-new-graphstore-core/["graph store"] of Gephi has become a completely independent module: it can be reused by another software.
+- Anybody can create new modules for Gephi! https://gephi.org/plugins/#/[They are called "plugins"]. They integrate with Gephi seamlessly.
+
+== created, maintained and documented by volunteers, for the community.
+
+//ST: created, maintained and documented by volunteers, for the community.
+
+- Gephi itself is open source and free
+- The https://consortium.gephi.org/[Gephi Consortium] was created to ensure Gephi will remain so
+- The https://github.com/gephi/gephi/graphs/contributors[original contributors and their followers] are volunteers
+- The support to users through http://forum-gephi.org/[forums], https://www.facebook.com/groups/gephi/[discussion groups] and https://gephi.org/users/[documents] is provided for free.
diff --git a/docs/generated-pdf/subdir/what-is-gephi-en_temp_slides.md b/docs/generated-pdf/subdir/what-is-gephi-en_temp_slides.md
new file mode 100644
index 0000000..d7a7e63
--- /dev/null
+++ b/docs/generated-pdf/subdir/what-is-gephi-en_temp_slides.md
@@ -0,0 +1,81 @@
+= What is Gephi?
+Clément Levallois
+2017-01-20
+
+last modified: {docdate}
+
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+
+== the leading desktop software to visualize and explore networks
+- more than 2 million downloads,
+- used by analysts, academics, journalists,
+- stable on Windows, Mac and Linux,
+- in continuous development
+
+
+
+== open source & free (including for commercial purposes)
+
+Gephi adopts a licencing model which makes it free to use, sell, or include the source code of Gephi in larger applications.
+
+Developers who modify the Gephi code base are required to make these changes public, to benefit the community.
+
+
+
+== "Photoshop^TM^ for graphs"
+
+- for networks of all types (social, semantic, biological, etc.)
+- attributes and properties of the network are made visible: not just the http://www.webopedia.com/quick_ref/topologies.asp[topology] of the network.
+- Gephi provides functions to explore the network: pan, filter, zoom, colorize, label, etc.
+- measures and metrics can be computed, and then rendered visually.
+
+
+
+== committed to quality and performance
+
+- Gephi won a http://www.oracle.com/technetwork/articles/java/dukeschoicewinners-171159.html[Duke Award in 2010]. Duke awards "recognize and honor extreme innovation in the world of Java technology".
+- Gephi evolved importantly since 2006 to adopt new, best performing techs for the visualization of very large graphs on the desktop
+- The code base of Gephi is maintained with the best standards to insure a https://gephi.wordpress.com/tag/code/[a stable application, ease of maintainance and modularity / extensibility].
+
+
+
+== modular and extensible
+
+- Gephi is "cut" in a large number of modules, each providing a specific function. This makes it easier to maintain and evolve the software, by working on independent parts.
+- Since version 0.9 the https://gephi.wordpress.com/2015/12/04/gephi-boosts-its-performance-with-new-graphstore-core/["graph store"] of Gephi has become a completely independent module: it can be reused by another software.
+- Anybody can create new modules for Gephi! https://gephi.org/plugins/#/[They are called "plugins"]. They integrate with Gephi seamlessly.
+
+
+== created, maintained and documented by volunteers, for the community.
+
+- Gephi itself is open source and free
+- The https://consortium.gephi.org/[Gephi Consortium] was created to ensure Gephi will remain so
+- The https://github.com/gephi/gephi/graphs/contributors[original contributors and their followers] are volunteers
+- The support to users through http://forum-gephi.org/[forums], https://www.facebook.com/groups/gephi/[discussion groups] and https://gephi.org/users/[documents] is provided for free.
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/working-from-the-source-en_temp_common.md b/docs/generated-pdf/subdir/working-from-the-source-en_temp_common.md
new file mode 100644
index 0000000..9cdfa48
--- /dev/null
+++ b/docs/generated-pdf/subdir/working-from-the-source-en_temp_common.md
@@ -0,0 +1,241 @@
+= Working from Gephi's source
+Clément Levallois
+2017-02-28
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Presentation of this tutorial
+//ST: Presentation of this tutorial
+
+//ST: !
+This tutorial explains how to get and modify Gephi's source code, and build Gephi from it.
+
+image::en/developers/use-the-source.jpg[align="center", title="The obligatory meme"]
+{nbsp} +
+
+//ST: !
+We will:
+
+- download the source code
+- run Gephi from the source
+- modify Gephi's openGL visualization engine
+- run Gephi in this modified version
+
+
+//ST: !
+The modified version of Gephi that we will implement will have for effect to add circles around nodes communities.
+
+This project originates from https://twitter.com/DataToViz/status/828840269072080896[Susie Lu's creation].
+
+We should get something like:
+
+//ST: !
+
+video::Y3jk-_QaFx4[youtube, height=315, width=560, align="center"]
+
+//PDF: image::en/developers/screenshot-encircling-pdf.png[align="center",title="Circles enclosing communities"]
+
+//PDF: link to animated version: https://www.youtube.com/watch?v=Y3jk-_QaFx4
+
+
+//ST: !
+
+For this tutorial you will need:
+
+- some knowledge of Java.
+
+- NetBeans installed (https://dl.dropboxusercontent.com/u/28091845/coursera/codapps/session%201/module%201%20-%20installing%20NetBeans%20ans%20Codename%20One%20on%20a%20PC.pdf[up to date instructions here]). Eclipse, IntelliJ or any other code editor won't do.
+
+- a good internet connection since we will download a lot of small dependencies.
+
+- no Github account is necessary (if you have one, fork Gephi before doing all the steps below).
+
+
+== Downloading the source code
+//ST: Downloading the source code
+
+//ST: !
+
+1. Open NetBeans.
+
+2. In NetBeans, go to `Team -> Git -> Clone`
+
+(can't find this menu in NetBeans? read an alternative just below)
+
+//ST: !
+
+image::en/developers/git-clone-gephi-0-en.png[align="center", title="Cloning the gephi source"]
+{nbsp} +
+
+//ST: !
+
+NOTE:: if you already have projects opened in NetBeans, the menu Team -> Git -> Clone is replaced by:
+
+ Team -> Remote -> Clone
+
+
+
+//ST: !
+
+image::Cloning-the-gephi-source.png[align="center", title="Cloning the gephi source"]
+{nbsp} +
+
+//ST: !
+- For `Repository URL`, put the address of the Gephi repository on Github, which is:
+
+https://github.com/gephi/gephi.git[https://github.com/gephi/gephi.git]
+
+- for `Clone into`, select a folder on your disk. No need to create a new folder inside, the cloning process will do it.
+
+- Github User and password can be left empty
+
+- Then click on `Next`
+
+//ST: !
+
+image::en/developers/git-clone-gephi-2-en.png[align="center", title="Select Master and click next"]
+{nbsp} +
+
+
+//ST: !
+image::en/developers/git-clone-gephi-3-en.png[align="center", title="Leave as it is and click on Finish"]
+{nbsp} +
+
+//ST: !
+At this step, NetBeans starts downloading the source code of Gephi - not all of it. Should take a couple of minutes, then:
+
+image::en/developers/git-clone-gephi-4-en.png[align="center", title="Click on Open Project"]
+{nbsp} +
+
+//ST: !
+Select (highlight in blue) the line mentioning Gephi, and click on "Open"
+
+image::en/developers/git-clone-gephi-5-en.png[align="center", title="Click on Open Project"]
+{nbsp} +
+
+//ST: !
+You should now see Gephi as a project on the left in NetBeans:
+
+image::en/developers/netbeans-gephi-source-1-en.png[align="center", title="Gephi project"]
+{nbsp} +
+
+//ST: !
+Right click on the project and select "Build with dependencies". This will download all the rest of the source, which can take roughly 5 to 20 minutes.
+
+image::en/developers/netbeans-gephi-source-2-en.png[align="center", title="Building the project"]
+{nbsp} +
+
+== Running Gephi from source
+//ST: Running Gephi from source
+
+//ST: !
+When the build is complete, expand the folder "Modules" of the Gephi project, and double click on "gephi-app":
+
+
+image::en/developers/netbeans-gephi-source-3-en.png[align="center", title="Opening the gephi-app submodule"]
+{nbsp} +
+//ST: !
+This opens a new project on the left of NetBeans, called "gephi-app". This is a submodule of Gephi, dedicated to managing its launch.
+
+image::Launching-Gephi.png[align="center", title="Launching Gephi"]
+{nbsp} +
+
+//ST: !
+
+And Gephi launches!
+
+Now that we know how to run Gephi from source, we can modify the source and see how it goes.
+
+== Modifying Gephi's openGL visualization engine
+//ST: Modifying Gephi's openGL visualization engine
+
+//ST: !
+We are going to add circles which will enclose groups of nodes (aka communities). In three steps:
+
+1. Open the `VisualizationImpl` module
+2. Create simple classes for the circles
+3. Add some code in the class which manages the display in OpenGL, so that circles get created
+
+//ST: !
+==== 1. Open the `VisualizationImpl` module
+
+In the gephi project, in the folder Modules, double click on the `VisualizationImpl` module:
+
+
+image::en/developers/enclosing-circles-1-en.png[align="center", title="Opening VisualizationImpl"]
+{nbsp} +
+
+//ST: !
+==== 2. Create simple classes for the circles
+
+Create a new package and add 3 classes in it: `Circle.java`, `Point.java`, `SmallestEnclosingCircle.java`.
+
+(I merely adapted these classes from https://www.nayuki.io/page/smallest-enclosing-circle[this website])
+
+image::en/developers/enclosing-circles-2-en.png[align="center", title="Three classes"]
+{nbsp} +
+
+//ST: !
+The code of these classes https://github.com/seinecle/gephi/tree/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/net/clementlevallois/enclosingcircles[can be found here].
+
+//ST: !
+==== 3. Add the circle creation logic to the class managing the OpenGL display
+
+Open the package `org.gephi.visualization.opengl`, and inside open `CompatibilityEngine.java`:
+
+image::opening-CompatibilityEngine.java.png[align="center", title="opening CompatibilityEngine.java"]
+{nbsp} +
+
+//ST: !
+No need to understand most of the code in this class.
+
+But we see that starting at https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L194[line 194], a loop on nodes starts:
+
+We will put nodes in a map (declared above, at https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L155[line 155]) where:
+
+- keys are the name of the cluster nodes belong too,
+- values are the Set of Nodes corresponding to the cluster.
+
+(yes, this implies the nodes must have an attribute called "Modularity Class" showing the cluster they belong to)
+
+//ST: !
+The code you should add to assign nodes to clusters in the map is in https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L197[lines 197-203]
+
+//ST: !
+
+Then, https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L210[lines 210-245] iterate on the map, and create the circles around clusters.
+
+The logic of the circle creation in OpenGL can be understood rather easily, by copying and modyfying the code used just above for the creation of nodes, and by looking at the online documentation on http://www.openglprojects.in/2014/03/draw-circle-opengl.html#gsc.tab=0[how to create shapes in OpenGL].
+
+//ST: !
+That's it. Now just run Gephi from source as we did before. From Gephi, open a network where nodes have a "Modularity Class" attribute, and Gephi will draw circles around nodes from the same clusters:
+
+//ST: !
+
+video::Y3jk-_QaFx4[youtube, height=315, width=560, align="center"]
+
+== More tutorials on using the source code of Gephi
+//ST: More tutorials on using the source code of Gephi
+//ST: !
+
+- https://github.com/gephi/gephi[The Gephi readme with some instructions for developers]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/working-from-the-source-en_temp_html.md b/docs/generated-pdf/subdir/working-from-the-source-en_temp_html.md
new file mode 100644
index 0000000..16619c7
--- /dev/null
+++ b/docs/generated-pdf/subdir/working-from-the-source-en_temp_html.md
@@ -0,0 +1,258 @@
+= Working from Gephi's source
+Clément Levallois
+2017-02-28
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Presentation of this tutorial
+//ST: Presentation of this tutorial
+
+//ST: !
+This tutorial explains how to get and modify Gephi's source code, and build Gephi from it.
+
+image::en/developers/use-the-source.jpg[align="center", title="The obligatory meme"]
+{nbsp} +
+
+//ST: !
+We will:
+
+- download the source code
+- run Gephi from the source
+- modify Gephi's openGL visualization engine
+- run Gephi in this modified version
+
+
+//ST: !
+The modified version of Gephi that we will implement will have for effect to add circles around nodes communities.
+
+This project originates from https://twitter.com/DataToViz/status/828840269072080896[Susie Lu's creation].
+
+We should get something like:
+
+//ST: !
+
+video::Y3jk-_QaFx4[youtube, height=315, width=560, align="center"]
+
+//PDF: image::en/developers/screenshot-encircling-pdf.png[align="center",title="Circles enclosing communities"]
+
+//PDF: link to animated version: https://www.youtube.com/watch?v=Y3jk-_QaFx4
+
+
+//ST: !
+
+For this tutorial you will need:
+
+- some knowledge of Java.
+
+- NetBeans installed (https://dl.dropboxusercontent.com/u/28091845/coursera/codapps/session%201/module%201%20-%20installing%20NetBeans%20ans%20Codename%20One%20on%20a%20PC.pdf[up to date instructions here]). Eclipse, IntelliJ or any other code editor won't do.
+
+- a good internet connection since we will download a lot of small dependencies.
+
+- no Github account is necessary (if you have one, fork Gephi before doing all the steps below).
+
+
+== Downloading the source code
+//ST: Downloading the source code
+
+//ST: !
+
+1. Open NetBeans.
+
+2. In NetBeans, go to `Team -> Git -> Clone`
+
+(can't find this menu in NetBeans? read an alternative just below)
+
+//ST: !
+
+image::en/developers/git-clone-gephi-0-en.png[align="center", title="Cloning the gephi source"]
+{nbsp} +
+
+//ST: !
+
+NOTE:: if you already have projects opened in NetBeans, the menu Team -> Git -> Clone is replaced by:
+
+ Team -> Remote -> Clone
+
+
+
+//ST: !
+
+image::Cloning-the-gephi-source.png[align="center", title="Cloning the gephi source"]
+{nbsp} +
+
+//ST: !
+- For `Repository URL`, put the address of the Gephi repository on Github, which is:
+
+https://github.com/gephi/gephi.git[https://github.com/gephi/gephi.git]
+
+- for `Clone into`, select a folder on your disk. No need to create a new folder inside, the cloning process will do it.
+
+- Github User and password can be left empty
+
+- Then click on `Next`
+
+//ST: !
+
+image::en/developers/git-clone-gephi-2-en.png[align="center", title="Select Master and click next"]
+{nbsp} +
+
+
+//ST: !
+image::en/developers/git-clone-gephi-3-en.png[align="center", title="Leave as it is and click on Finish"]
+{nbsp} +
+
+//ST: !
+At this step, NetBeans starts downloading the source code of Gephi - not all of it. Should take a couple of minutes, then:
+
+image::en/developers/git-clone-gephi-4-en.png[align="center", title="Click on Open Project"]
+{nbsp} +
+
+//ST: !
+Select (highlight in blue) the line mentioning Gephi, and click on "Open"
+
+image::en/developers/git-clone-gephi-5-en.png[align="center", title="Click on Open Project"]
+{nbsp} +
+
+//ST: !
+You should now see Gephi as a project on the left in NetBeans:
+
+image::en/developers/netbeans-gephi-source-1-en.png[align="center", title="Gephi project"]
+{nbsp} +
+
+//ST: !
+Right click on the project and select "Build with dependencies". This will download all the rest of the source, which can take roughly 5 to 20 minutes.
+
+image::en/developers/netbeans-gephi-source-2-en.png[align="center", title="Building the project"]
+{nbsp} +
+
+== Running Gephi from source
+//ST: Running Gephi from source
+
+//ST: !
+When the build is complete, expand the folder "Modules" of the Gephi project, and double click on "gephi-app":
+
+
+image::en/developers/netbeans-gephi-source-3-en.png[align="center", title="Opening the gephi-app submodule"]
+{nbsp} +
+//ST: !
+This opens a new project on the left of NetBeans, called "gephi-app". This is a submodule of Gephi, dedicated to managing its launch.
+
+image::Launching-Gephi.png[align="center", title="Launching Gephi"]
+{nbsp} +
+
+//ST: !
+
+And Gephi launches!
+
+Now that we know how to run Gephi from source, we can modify the source and see how it goes.
+
+== Modifying Gephi's openGL visualization engine
+//ST: Modifying Gephi's openGL visualization engine
+
+//ST: !
+We are going to add circles which will enclose groups of nodes (aka communities). In three steps:
+
+1. Open the `VisualizationImpl` module
+2. Create simple classes for the circles
+3. Add some code in the class which manages the display in OpenGL, so that circles get created
+
+//ST: !
+==== 1. Open the `VisualizationImpl` module
+
+In the gephi project, in the folder Modules, double click on the `VisualizationImpl` module:
+
+
+image::en/developers/enclosing-circles-1-en.png[align="center", title="Opening VisualizationImpl"]
+{nbsp} +
+
+//ST: !
+==== 2. Create simple classes for the circles
+
+Create a new package and add 3 classes in it: `Circle.java`, `Point.java`, `SmallestEnclosingCircle.java`.
+
+(I merely adapted these classes from https://www.nayuki.io/page/smallest-enclosing-circle[this website])
+
+image::en/developers/enclosing-circles-2-en.png[align="center", title="Three classes"]
+{nbsp} +
+
+//ST: !
+The code of these classes https://github.com/seinecle/gephi/tree/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/net/clementlevallois/enclosingcircles[can be found here].
+
+//ST: !
+==== 3. Add the circle creation logic to the class managing the OpenGL display
+
+Open the package `org.gephi.visualization.opengl`, and inside open `CompatibilityEngine.java`:
+
+image::opening-CompatibilityEngine.java.png[align="center", title="opening CompatibilityEngine.java"]
+{nbsp} +
+
+//ST: !
+No need to understand most of the code in this class.
+
+But we see that starting at https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L194[line 194], a loop on nodes starts:
+
+We will put nodes in a map (declared above, at https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L155[line 155]) where:
+
+- keys are the name of the cluster nodes belong too,
+- values are the Set of Nodes corresponding to the cluster.
+
+(yes, this implies the nodes must have an attribute called "Modularity Class" showing the cluster they belong to)
+
+//ST: !
+The code you should add to assign nodes to clusters in the map is in https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L197[lines 197-203]
+
+//ST: !
+
+Then, https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L210[lines 210-245] iterate on the map, and create the circles around clusters.
+
+The logic of the circle creation in OpenGL can be understood rather easily, by copying and modyfying the code used just above for the creation of nodes, and by looking at the online documentation on http://www.openglprojects.in/2014/03/draw-circle-opengl.html#gsc.tab=0[how to create shapes in OpenGL].
+
+//ST: !
+That's it. Now just run Gephi from source as we did before. From Gephi, open a network where nodes have a "Modularity Class" attribute, and Gephi will draw circles around nodes from the same clusters:
+
+//ST: !
+
+video::Y3jk-_QaFx4[youtube, height=315, width=560, align="center"]
+
+== More tutorials on using the source code of Gephi
+//ST: More tutorials on using the source code of Gephi
+//ST: !
+
+- https://github.com/gephi/gephi[The Gephi readme with some instructions for developers]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/working-from-the-source-en_temp_pdf.md b/docs/generated-pdf/subdir/working-from-the-source-en_temp_pdf.md
new file mode 100644
index 0000000..6652e45
--- /dev/null
+++ b/docs/generated-pdf/subdir/working-from-the-source-en_temp_pdf.md
@@ -0,0 +1,241 @@
+= Working from Gephi's source
+Clément Levallois
+2017-02-28
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Presentation of this tutorial
+//ST: Presentation of this tutorial
+
+//ST: !
+This tutorial explains how to get and modify Gephi's source code, and build Gephi from it.
+
+image::en/developers/use-the-source.jpg[align="center", title="The obligatory meme"]
+{nbsp} +
+
+//ST: !
+We will:
+
+- download the source code
+- run Gephi from the source
+- modify Gephi's openGL visualization engine
+- run Gephi in this modified version
+
+
+//ST: !
+The modified version of Gephi that we will implement will have for effect to add circles around nodes communities.
+
+This project originates from https://twitter.com/DataToViz/status/828840269072080896[Susie Lu's creation].
+
+We should get something like:
+
+//ST: !
+
+video::Y3jk-_QaFx4[youtube, height=315, width=560, align="center"]
+
+image::en/developers/screenshot-encircling-pdf.png[align="center",title="Circles enclosing communities"]
+
+link to animated version: https://www.youtube.com/watch?v=Y3jk-_QaFx4
+
+
+//ST: !
+
+For this tutorial you will need:
+
+- some knowledge of Java.
+
+- NetBeans installed (https://dl.dropboxusercontent.com/u/28091845/coursera/codapps/session%201/module%201%20-%20installing%20NetBeans%20ans%20Codename%20One%20on%20a%20PC.pdf[up to date instructions here]). Eclipse, IntelliJ or any other code editor won't do.
+
+- a good internet connection since we will download a lot of small dependencies.
+
+- no Github account is necessary (if you have one, fork Gephi before doing all the steps below).
+
+
+== Downloading the source code
+//ST: Downloading the source code
+
+//ST: !
+
+1. Open NetBeans.
+
+2. In NetBeans, go to `Team -> Git -> Clone`
+
+(can't find this menu in NetBeans? read an alternative just below)
+
+//ST: !
+
+image::en/developers/git-clone-gephi-0-en.png[align="center", title="Cloning the gephi source"]
+{nbsp} +
+
+//ST: !
+
+NOTE:: if you already have projects opened in NetBeans, the menu Team -> Git -> Clone is replaced by:
+
+ Team -> Remote -> Clone
+
+
+
+//ST: !
+
+image::Cloning-the-gephi-source.png[align="center", title="Cloning the gephi source"]
+{nbsp} +
+
+//ST: !
+- For `Repository URL`, put the address of the Gephi repository on Github, which is:
+
+https://github.com/gephi/gephi.git[https://github.com/gephi/gephi.git]
+
+- for `Clone into`, select a folder on your disk. No need to create a new folder inside, the cloning process will do it.
+
+- Github User and password can be left empty
+
+- Then click on `Next`
+
+//ST: !
+
+image::en/developers/git-clone-gephi-2-en.png[align="center", title="Select Master and click next"]
+{nbsp} +
+
+
+//ST: !
+image::en/developers/git-clone-gephi-3-en.png[align="center", title="Leave as it is and click on Finish"]
+{nbsp} +
+
+//ST: !
+At this step, NetBeans starts downloading the source code of Gephi - not all of it. Should take a couple of minutes, then:
+
+image::en/developers/git-clone-gephi-4-en.png[align="center", title="Click on Open Project"]
+{nbsp} +
+
+//ST: !
+Select (highlight in blue) the line mentioning Gephi, and click on "Open"
+
+image::en/developers/git-clone-gephi-5-en.png[align="center", title="Click on Open Project"]
+{nbsp} +
+
+//ST: !
+You should now see Gephi as a project on the left in NetBeans:
+
+image::en/developers/netbeans-gephi-source-1-en.png[align="center", title="Gephi project"]
+{nbsp} +
+
+//ST: !
+Right click on the project and select "Build with dependencies". This will download all the rest of the source, which can take roughly 5 to 20 minutes.
+
+image::en/developers/netbeans-gephi-source-2-en.png[align="center", title="Building the project"]
+{nbsp} +
+
+== Running Gephi from source
+//ST: Running Gephi from source
+
+//ST: !
+When the build is complete, expand the folder "Modules" of the Gephi project, and double click on "gephi-app":
+
+
+image::en/developers/netbeans-gephi-source-3-en.png[align="center", title="Opening the gephi-app submodule"]
+{nbsp} +
+//ST: !
+This opens a new project on the left of NetBeans, called "gephi-app". This is a submodule of Gephi, dedicated to managing its launch.
+
+image::Launching-Gephi.png[align="center", title="Launching Gephi"]
+{nbsp} +
+
+//ST: !
+
+And Gephi launches!
+
+Now that we know how to run Gephi from source, we can modify the source and see how it goes.
+
+== Modifying Gephi's openGL visualization engine
+//ST: Modifying Gephi's openGL visualization engine
+
+//ST: !
+We are going to add circles which will enclose groups of nodes (aka communities). In three steps:
+
+1. Open the `VisualizationImpl` module
+2. Create simple classes for the circles
+3. Add some code in the class which manages the display in OpenGL, so that circles get created
+
+//ST: !
+==== 1. Open the `VisualizationImpl` module
+
+In the gephi project, in the folder Modules, double click on the `VisualizationImpl` module:
+
+
+image::en/developers/enclosing-circles-1-en.png[align="center", title="Opening VisualizationImpl"]
+{nbsp} +
+
+//ST: !
+==== 2. Create simple classes for the circles
+
+Create a new package and add 3 classes in it: `Circle.java`, `Point.java`, `SmallestEnclosingCircle.java`.
+
+(I merely adapted these classes from https://www.nayuki.io/page/smallest-enclosing-circle[this website])
+
+image::en/developers/enclosing-circles-2-en.png[align="center", title="Three classes"]
+{nbsp} +
+
+//ST: !
+The code of these classes https://github.com/seinecle/gephi/tree/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/net/clementlevallois/enclosingcircles[can be found here].
+
+//ST: !
+==== 3. Add the circle creation logic to the class managing the OpenGL display
+
+Open the package `org.gephi.visualization.opengl`, and inside open `CompatibilityEngine.java`:
+
+image::opening-CompatibilityEngine.java.png[align="center", title="opening CompatibilityEngine.java"]
+{nbsp} +
+
+//ST: !
+No need to understand most of the code in this class.
+
+But we see that starting at https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L194[line 194], a loop on nodes starts:
+
+We will put nodes in a map (declared above, at https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L155[line 155]) where:
+
+- keys are the name of the cluster nodes belong too,
+- values are the Set of Nodes corresponding to the cluster.
+
+(yes, this implies the nodes must have an attribute called "Modularity Class" showing the cluster they belong to)
+
+//ST: !
+The code you should add to assign nodes to clusters in the map is in https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L197[lines 197-203]
+
+//ST: !
+
+Then, https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L210[lines 210-245] iterate on the map, and create the circles around clusters.
+
+The logic of the circle creation in OpenGL can be understood rather easily, by copying and modyfying the code used just above for the creation of nodes, and by looking at the online documentation on http://www.openglprojects.in/2014/03/draw-circle-opengl.html#gsc.tab=0[how to create shapes in OpenGL].
+
+//ST: !
+That's it. Now just run Gephi from source as we did before. From Gephi, open a network where nodes have a "Modularity Class" attribute, and Gephi will draw circles around nodes from the same clusters:
+
+//ST: !
+
+video::Y3jk-_QaFx4[youtube, height=315, width=560, align="center"]
+
+== More tutorials on using the source code of Gephi
+//ST: More tutorials on using the source code of Gephi
+//ST: !
+
+- https://github.com/gephi/gephi[The Gephi readme with some instructions for developers]
+
+== the end
+
+//ST: The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/working-from-the-source-en_temp_slides.md b/docs/generated-pdf/subdir/working-from-the-source-en_temp_slides.md
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+= Working from Gephi's source
+Clément Levallois
+2017-02-28
+
+last modified: {docdate}
+
+:icons!:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Presentation of this tutorial
+
+== !
+This tutorial explains how to get and modify Gephi's source code, and build Gephi from it.
+
+[.stretch]
+image::en/developers/use-the-source.jpg[align="center", title="The obligatory meme"]
+
+
+== !
+We will:
+
+- download the source code
+- run Gephi from the source
+- modify Gephi's openGL visualization engine
+- run Gephi in this modified version
+
+
+== !
+The modified version of Gephi that we will implement will have for effect to add circles around nodes communities.
+
+This project originates from https://twitter.com/DataToViz/status/828840269072080896[Susie Lu's creation].
+
+We should get something like:
+
+== !
+
+video::Y3jk-_QaFx4[youtube, height=315, width=560, align="center"]
+
+//PDF: image::en/developers/screenshot-encircling-pdf.png[align="center",title="Circles enclosing communities"]
+
+//PDF: link to animated version: https://www.youtube.com/watch?v=Y3jk-_QaFx4
+
+
+== !
+
+For this tutorial you will need:
+
+- some knowledge of Java.
+
+- NetBeans installed (https://dl.dropboxusercontent.com/u/28091845/coursera/codapps/session%201/module%201%20-%20installing%20NetBeans%20ans%20Codename%20One%20on%20a%20PC.pdf[up to date instructions here]). Eclipse, IntelliJ or any other code editor won't do.
+
+- a good internet connection since we will download a lot of small dependencies.
+
+- no Github account is necessary (if you have one, fork Gephi before doing all the steps below).
+
+
+== Downloading the source code
+
+== !
+
+1. Open NetBeans.
+
+2. In NetBeans, go to `Team -> Git -> Clone`
+
+(can't find this menu in NetBeans? read an alternative just below)
+
+== !
+
+[.stretch]
+image::en/developers/git-clone-gephi-0-en.png[align="center", title="Cloning the gephi source"]
+
+
+== !
+
+NOTE:: if you already have projects opened in NetBeans, the menu Team -> Git -> Clone is replaced by:
+
+ Team -> Remote -> Clone
+
+
+
+== !
+
+[.stretch]
+image::Cloning-the-gephi-source.png[align="center", title="Cloning the gephi source"]
+
+
+== !
+- For `Repository URL`, put the address of the Gephi repository on Github, which is:
+
+https://github.com/gephi/gephi.git[https://github.com/gephi/gephi.git]
+
+- for `Clone into`, select a folder on your disk. No need to create a new folder inside, the cloning process will do it.
+
+- Github User and password can be left empty
+
+- Then click on `Next`
+
+== !
+
+[.stretch]
+image::en/developers/git-clone-gephi-2-en.png[align="center", title="Select Master and click next"]
+
+
+
+== !
+[.stretch]
+image::en/developers/git-clone-gephi-3-en.png[align="center", title="Leave as it is and click on Finish"]
+
+
+== !
+At this step, NetBeans starts downloading the source code of Gephi - not all of it. Should take a couple of minutes, then:
+
+[.stretch]
+image::en/developers/git-clone-gephi-4-en.png[align="center", title="Click on Open Project"]
+
+
+== !
+Select (highlight in blue) the line mentioning Gephi, and click on "Open"
+
+[.stretch]
+image::en/developers/git-clone-gephi-5-en.png[align="center", title="Click on Open Project"]
+
+
+== !
+You should now see Gephi as a project on the left in NetBeans:
+
+[.stretch]
+image::en/developers/netbeans-gephi-source-1-en.png[align="center", title="Gephi project"]
+
+
+== !
+Right click on the project and select "Build with dependencies". This will download all the rest of the source, which can take roughly 5 to 20 minutes.
+
+[.stretch]
+image::en/developers/netbeans-gephi-source-2-en.png[align="center", title="Building the project"]
+
+
+== Running Gephi from source
+
+== !
+When the build is complete, expand the folder "Modules" of the Gephi project, and double click on "gephi-app":
+
+
+[.stretch]
+image::en/developers/netbeans-gephi-source-3-en.png[align="center", title="Opening the gephi-app submodule"]
+
+== !
+This opens a new project on the left of NetBeans, called "gephi-app". This is a submodule of Gephi, dedicated to managing its launch.
+
+[.stretch]
+image::Launching-Gephi.png[align="center", title="Launching Gephi"]
+
+
+== !
+
+And Gephi launches!
+
+Now that we know how to run Gephi from source, we can modify the source and see how it goes.
+
+== Modifying Gephi's openGL visualization engine
+
+== !
+We are going to add circles which will enclose groups of nodes (aka communities). In three steps:
+
+1. Open the `VisualizationImpl` module
+2. Create simple classes for the circles
+3. Add some code in the class which manages the display in OpenGL, so that circles get created
+
+== !
+==== 1. Open the `VisualizationImpl` module
+
+In the gephi project, in the folder Modules, double click on the `VisualizationImpl` module:
+
+
+[.stretch]
+image::en/developers/enclosing-circles-1-en.png[align="center", title="Opening VisualizationImpl"]
+
+
+== !
+==== 2. Create simple classes for the circles
+
+Create a new package and add 3 classes in it: `Circle.java`, `Point.java`, `SmallestEnclosingCircle.java`.
+
+(I merely adapted these classes from https://www.nayuki.io/page/smallest-enclosing-circle[this website])
+
+[.stretch]
+image::en/developers/enclosing-circles-2-en.png[align="center", title="Three classes"]
+
+
+== !
+The code of these classes https://github.com/seinecle/gephi/tree/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/net/clementlevallois/enclosingcircles[can be found here].
+
+== !
+==== 3. Add the circle creation logic to the class managing the OpenGL display
+
+Open the package `org.gephi.visualization.opengl`, and inside open `CompatibilityEngine.java`:
+
+[.stretch]
+image::opening-CompatibilityEngine.java.png[align="center", title="opening CompatibilityEngine.java"]
+
+
+== !
+No need to understand most of the code in this class.
+
+But we see that starting at https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L194[line 194], a loop on nodes starts:
+
+We will put nodes in a map (declared above, at https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L155[line 155]) where:
+
+- keys are the name of the cluster nodes belong too,
+- values are the Set of Nodes corresponding to the cluster.
+
+(yes, this implies the nodes must have an attribute called "Modularity Class" showing the cluster they belong to)
+
+== !
+The code you should add to assign nodes to clusters in the map is in https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L197[lines 197-203]
+
+== !
+
+Then, https://github.com/seinecle/gephi/blob/enclosing-circles-opengl/modules/VisualizationImpl/src/main/java/org/gephi/visualization/opengl/CompatibilityEngine.java#L210[lines 210-245] iterate on the map, and create the circles around clusters.
+
+The logic of the circle creation in OpenGL can be understood rather easily, by copying and modyfying the code used just above for the creation of nodes, and by looking at the online documentation on http://www.openglprojects.in/2014/03/draw-circle-opengl.html#gsc.tab=0[how to create shapes in OpenGL].
+
+== !
+That's it. Now just run Gephi from source as we did before. From Gephi, open a network where nodes have a "Modularity Class" attribute, and Gephi will draw circles around nodes from the same clusters:
+
+== !
+
+video::Y3jk-_QaFx4[youtube, height=315, width=560, align="center"]
+
+== More tutorials on using the source code of Gephi
+== !
+
+- https://github.com/gephi/gephi[The Gephi readme with some instructions for developers]
+
+
+== The end!
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/working-with-text-en_temp_common.md b/docs/generated-pdf/subdir/working-with-text-en_temp_common.md
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+= Working with text in Gephi
+Clément Levallois
+2017-03-07
+
+last modified: {docdate}
+
+:icons!:
+:asciimath:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Presentation of this tutorial
+//ST: Presentation of this tutorial
+
+//ST: !
+This tutorial explains how to draw "semantic networks" like this one:
+
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="a semantic network"]
+{nbsp} +
+
+//ST: !
+
+We call "semantic network" a visualization where textual items (words, expressions) are connected to each others, like above.
+
+We will see in turn:
+
+//ST: !
+
+- why are semantic networks interesting
+- how to create a semantic network
+- tips and tricks to visualize semantic networks in the best possible way in Gephi
+
+
+== Why semantic networks?
+//ST: Why semantic networks?
+//ST: !
+
+A text, or many texts, can be hard to summarize.
+
+Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" they form.
+
+//ST: !
+
+Often, a cluster of terms characterizes a topic.
+Hence, converting a text into a semantic network helps detecting topics in the text, from micro-topics to the general themes discussed in the documents.
+
+//ST: !
+
+Semantic networks are regular networks, where:
+
+- nodes are words ("USA") or groups of words ("United States of America")
+
+- relations are, usually, signifying co-occurrences: two words are connected if they appear in the same document, or in the same paragraph, or same sentence... you decide.
+
+//ST: !
+
+It means that if you have a textual network, you can visualize it with Gephi just like any other network.
+
+Yet, not everything is the same, and this tutorial provides tips and tricks on why textual data can be a bit different than other data.
+
+== Choosing what a "term" is in a semantic network
+//ST: Choosing what a "term" is in a semantic network
+//ST: !
+
+The starting point can be: a term is a single word. So in this sentence, we would have 7 terms:
+
+ My sister lives in the United States (7 words -> 7 terms)
+
+This means that each single term is a meaningful semantic unit.
+
+This approach is simple but not great. Look again at the sentence:
+
+//ST: !
+
+ My sister lives in the United States
+
+1. `My`, `in`, `the` are frequent terms which have no special significance: they should probably be discarded
+2. `United` and `States` are meaningful separately, but here they should probably be considered together: `United States`
+3. `lives` is the conjugated form of the verb `to live`. In a network, it would make sense to regroup `live`, `lives` and `lived` as one single node.
+
+Analysts, facing each of these issues, have imagined several solutions:
+
+//ST: !
+==== 1. Removing "stopwords"
+//ST: !
+
+To remove these little terms without informational value, the most basic approach is to keep a list of them, and remove any word from the text which belongs to this list.
+
+You can find a list of these useless terms in many languages, called "stopwords", http://www.ranks.nl/stopwords/[on this website].
+
+//ST: !
+[start=2]
+==== 2. Considering "n-grams"
+//ST: !
+
+So, `United States` should probably be a meaningful unit, not just `United` and `States`.
+Because `United States` is composed of 2 terms, it is called a "bi-gram".
+
+Trigrams are interesting as well obviously (eg, `chocolate ice cream`).
+
+//ST: !
+
+People often stop there, but quadrigrams can be meaningful as well, if less frequent: `United States of America`, `functional magnetic resonance imaging`, `The New York Times`, etc.
+
+Many tools exist to extract n-grams from texts, for example http://homepages.inf.ed.ac.uk/lzhang10/ngram.html[these programs which are under a free license].
+
+//ST: !
+[start=2]
+==== 2 bis. Considering "noun phrases"
+//ST: !
+
+Another approach to go beyond single word terms (`United`, `States`) takes a different approach than n-grams. It says:
+
+ "delete all in the text except for groups of words made of nouns and adjectives, ending by a noun"
+
+-> (these are called, a bit improperly, "noun phrases")
+
+Take `United States`: it is a noun (`States`) preceded by an adjective (`United`). It will be considered as a valid term.
+
+//ST: !
+
+This approach is interesting (implemented for example in the software http://www.vosviewer.com[Vosviewer]), but it has drawbacks:
+
+- you need to detect adjectives and nouns in your text. This is language dependent (French put adjectives after nouns, for instance), and the processing is slow for large corpora.
+
+- what about verbs, and noun phrases comprising non adjectives, such as "United States *of* America"? These are not going to be included in the network.
+
+//ST: !
+[start=3]
+==== 3. Stemming and lemmatization
+//ST: !
+
+`live`, `lives`, `lived`: in a semantic network, it is probably useless to have 3 nodes, one for each of these 3 forms of the same root.
+
+- Stemming consists in chopping the end of the words, so that here, we would have only `live`.
+- Lemmatization is the same, but in a more subtle way: it takes grammar into account. So, "good" and better" would be reduced to "good" because there is the same basic semantic unit behind these two words, even if their lettering differ completely.
+
+//ST: !
+
+A tool performing lemmatization is https://textgrid.de/en/[TextGrid].
+It has many functions for textual analysis, and lemmatization https://wiki.de.dariah.eu/display/TextGrid/The+Lemmatizer+Tool[is explained there].
+
+== Should we represent all terms in a semantic network?
+//ST: Should we represent all terms in a semantic network?
+
+//ST: !
+We have seen that some words are more interesting than others in a corpus:
+
+- stopwords should be removed,
+- some varieties of words (`lived`, `lives`) could be grouped together (`live`).
+- sequences of words (`baby phone`) can be added because they mean more than their words taken separately (`baby`, `phone`)
+
+//ST: !
+Once this is done, we have transformed the text into plenty of words to represent. Should they all be included in the network?
+
+Imagine we have a word appearing just once, in a single footnote of a text long of 2,000 pages.
+Should this word appear? Probably not.
+
+Which rule to apply to keep or leave out a word?
+
+//ST: !
+==== 1. Start with: how many words can fit in your visualization?
+//ST: !
+
+A starting point can be the number of words you would like to see on a visualization. *A ball park figure is 300 words max*:
+
+- it already fills in all the space of a computer screen.
+- 300 words provides enough information to allow micro-topics of a text to be distinguished
+
+//ST: !
+
+More words can be crammed in a visualization, but in this case the viewer would have to take time zooming in and out, panning to explore the visualization.
+The viewer transforms into an analyst, instead of a regular reader.
+
+//ST: !
+==== 2. Representing only the most frequent terms
+//ST: !
+
+If ~ 300 words would fit in the visualization of the network, and the text you start with contains 5,000 different words: which 300 words should be selected?
+
+To visualize the semantic network *for a long, single text* the straightforward approach consists in picking the 300 most frequent words (or n-grams, see above).
+
+In the case of a collection of texts to visualize (several documents instead of one), two possibilities:
+
+//ST: !
+
+1. Either you also take the most frequent terms across these documents, like before
+
+2. Or you can apply a more subtle rule called "tf-idf", detailed below.
+
+//ST: tf-idf
+
+The idea with tf-idf is that terms which appear in all documents are not interesting, because they are so ubiquitous.
+
+Example: you retrieve all the webpages mentioning the word `Gephi`, and then want to visualize the semantic network of the texts contained in these webpages.
+
+//ST: !
+
+-> by definition, all these webpages will mention Gephi, so Gephi will probably be the most frequent term.
+
+-> so your network will end up with a node "Gephi" connected to many other terms, but you actually knew that. Boring.
+
+-> terms used in all web pages are less interesting to you than terms which are used frequently, but not uniformly accross webpages.
+
+//ST: !
+
+Applying the tf-idf correction will highlight terms which are frequently used within some texts, but not used in many texts.
+
+(to go further, here is a webpage giving a simple example: http://www.tfidf.com/)
+
+//ST: !
+So, should you visualize the most frequent words in your corpus, or the words which rank highest according to tf-idf?
+
+Both are interesting, as they show a different info. I'd suggest that the simple frequency count is easier to interpret.
+
+tf-idf can be left for specialists of the textual data under consideration, after they have been presented with the simple frequency count version.
+
+== Computing connections (edges) in the network
+//ST: Computing connections (edges) in the network
+
+//ST: !
+We now have extracted the most interesting / meaningful terms from the text.
+How to decide which connections make sense between them?
+
+//ST: !
+==== 1. Co-occurrences
+//ST: !
+
+Connections between terms are usually drawn from co-occurrences: two terms will be connected if they appear next to each other in some pre-defined unit of text:
+
+- in the same sentence
+- in the same paragraph
+- in the same document (if the corpus is made of several documents)
+
+(note on vocabulary: in the following, we will call this a "unit of text").
+
+//ST: !
+For example, in bibliometrics (the study of the publications produced by scientists), this could give:
+
+- collect *abstracts* (short summaries) of all scientific articles discussing "nano-technologies".
+- so, abstracts are our units of text here.
+- two terms will be connected if they frequently appear *in the same abstracts*.
+
+//ST: !
+==== 2. What "weight" for the edges?
+//ST: !
+
+An edge between two terms will have:
+
+- weight of "1" if these two terms co-occur in just one unit of text.
+- weight of "2" if they co-occur in two units of text.
+- etc...
+
+The logic is simple, and yet there are some refinements to discuss. It will be up to you to decide what's preferable:
+
+//ST: !
+===== If 2 terms appear several times *in a given unit of text*, should their co-occurences be counted several times?
+//ST: !
+
+An example to clarify. Let's imagine that we are interested in webpages discussing nanotechnology.
+We want to draw the semantic network of the vocabulary used in these web pages.
+
+A co-occurrence is: when 2 terms are used on the same web page.
+
+Among the pages we collected, there is the Wikipedia page discussing nanotechnology:
+
+//ST: !
+
+[quote, 'https://en.wikipedia.org/wiki/Nanotechnology[Wikipedia]']
+____
+[red]#Nanotechnology# ("nanotech") is manipulation of matter on an atomic, [blue]#molecular#, and supramolecular scale.
+The earliest, widespread description of [red]#nanotechnology# referred to the particular technological goal of precisely manipulating atoms and molecules for fabrication of macroscale products, also now referred to as [blue]#molecular# [red]#nanotechnology#
+____
+
+//ST: !
+The question is:
+
+- should I count only *one* co-occurrence between `molecular` and `nanotechnology`, because it happened on this one web page?
+- or should I consider that `molecular` appears twice on this page, and `nanotechnology` three times, so *multiple* co-occurrences between these 2 terms should be counted, just on this page already?
+
+There is no exact response, and you can experiment with both possibilities.
+
+//ST: !
+===== If two terms are very frequent, is their co-occurrence really of interest?
+//ST: !
+
+Example:
+
+Chun-Yuen Teng, Yu-Ru Lin and Lada Adamic have studied (using Gephi!) https://arxiv.org/abs/1111.3919[the pairing of ingredients in cooking recipes].
+
+So, in their study the unit of text was the "recipe", and the terms in the semantic network where ingredients in all these recipes.
+
+//ST: !
+Just because they are so common, some ingredients (like `flour`, `sugar`, `salt`) are bound to appear more frequently in the same recipes (to co-occur), than infrequent ingredients.
+
+The authors of this study chose to highlight *complementary ingredients*: some ingredients appear often used together in the same recipes, _even if they are ingredients which are quite rarely used_.
+
+"Complementary" here means that these ingredients have some interesting relationship: when one is used, the other "must" be used as well.
+
+//ST: !
+
+If we just count co-occurrences, this special relationship between infrequent complementary ingredients will be lost: by definition, 2 infrequent ingredients can't co-occurr often.
+
+To fix this, a solution consists in comparing how many times the 2 ingredients co-occur, with how frequent they are in all recipes:
+
+-> ingredients co-occurring _each and every time they are used_ will have a large edge weight,
+
+-> ingredients co-occuring many times, _but also appearing many times in different recipes_, will get a low edge weight.
+
+//ST: !
+
+A simple formula does this operation. For ingredients A and B:
+
+ weight of edge between A and B =
+ nb of recipes where A & B co-occur
+ divided by
+ (total nb of recipes where A appear x total nb of recipes where B appear)
+
+//ST: !
+
+Logs are often added to this formula, which is called "Pointwise mutual information":
+
+[asciimath]
+++++
+PMI = log((p(A, B)) /(p(A) p(B)))
+++++
+
+//ST: !
+We now have nodes and their relations: a semantic network. Let's see now how to visualize it in Gephi.
+
+
+== Visualizing semantic networks with Gephi
+//ST: Visualizing semantic networks with Gephi
+//ST: !
+
+
+
+== (to be continued)
+//ST: (to be continued)
+
+
+== More tutorials on working with semantic networks
+//ST: More tutorials on working with semantic networks
+//ST: !
+
+
+== the end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
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--- /dev/null
+++ b/docs/generated-pdf/subdir/working-with-text-en_temp_html.md
@@ -0,0 +1,378 @@
+= Working with text in Gephi
+Clément Levallois
+2017-03-07
+
+last modified: {docdate}
+
+:icons!:
+:asciimath:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Presentation of this tutorial
+//ST: Presentation of this tutorial
+
+//ST: !
+This tutorial explains how to draw "semantic networks" like this one:
+
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="a semantic network"]
+{nbsp} +
+
+//ST: !
+
+We call "semantic network" a visualization where textual items (words, expressions) are connected to each others, like above.
+
+We will see in turn:
+
+//ST: !
+
+- why are semantic networks interesting
+- how to create a semantic network
+- tips and tricks to visualize semantic networks in the best possible way in Gephi
+
+
+== Why semantic networks?
+//ST: Why semantic networks?
+//ST: !
+
+A text, or many texts, can be hard to summarize.
+
+Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" they form.
+
+//ST: !
+
+Often, a cluster of terms characterizes a topic.
+Hence, converting a text into a semantic network helps detecting topics in the text, from micro-topics to the general themes discussed in the documents.
+
+//ST: !
+
+Semantic networks are regular networks, where:
+
+- nodes are words ("USA") or groups of words ("United States of America")
+
+- relations are, usually, signifying co-occurrences: two words are connected if they appear in the same document, or in the same paragraph, or same sentence... you decide.
+
+//ST: !
+
+It means that if you have a textual network, you can visualize it with Gephi just like any other network.
+
+Yet, not everything is the same, and this tutorial provides tips and tricks on why textual data can be a bit different than other data.
+
+== Choosing what a "term" is in a semantic network
+//ST: Choosing what a "term" is in a semantic network
+//ST: !
+
+The starting point can be: a term is a single word. So in this sentence, we would have 7 terms:
+
+ My sister lives in the United States (7 words -> 7 terms)
+
+This means that each single term is a meaningful semantic unit.
+
+This approach is simple but not great. Look again at the sentence:
+
+//ST: !
+
+ My sister lives in the United States
+
+1. `My`, `in`, `the` are frequent terms which have no special significance: they should probably be discarded
+2. `United` and `States` are meaningful separately, but here they should probably be considered together: `United States`
+3. `lives` is the conjugated form of the verb `to live`. In a network, it would make sense to regroup `live`, `lives` and `lived` as one single node.
+
+Analysts, facing each of these issues, have imagined several solutions:
+
+//ST: !
+==== 1. Removing "stopwords"
+//ST: !
+
+To remove these little terms without informational value, the most basic approach is to keep a list of them, and remove any word from the text which belongs to this list.
+
+You can find a list of these useless terms in many languages, called "stopwords", http://www.ranks.nl/stopwords/[on this website].
+
+//ST: !
+[start=2]
+==== 2. Considering "n-grams"
+//ST: !
+
+So, `United States` should probably be a meaningful unit, not just `United` and `States`.
+Because `United States` is composed of 2 terms, it is called a "bi-gram".
+
+Trigrams are interesting as well obviously (eg, `chocolate ice cream`).
+
+//ST: !
+
+People often stop there, but quadrigrams can be meaningful as well, if less frequent: `United States of America`, `functional magnetic resonance imaging`, `The New York Times`, etc.
+
+Many tools exist to extract n-grams from texts, for example http://homepages.inf.ed.ac.uk/lzhang10/ngram.html[these programs which are under a free license].
+
+//ST: !
+[start=2]
+==== 2 bis. Considering "noun phrases"
+//ST: !
+
+Another approach to go beyond single word terms (`United`, `States`) takes a different approach than n-grams. It says:
+
+ "delete all in the text except for groups of words made of nouns and adjectives, ending by a noun"
+
+-> (these are called, a bit improperly, "noun phrases")
+
+Take `United States`: it is a noun (`States`) preceded by an adjective (`United`). It will be considered as a valid term.
+
+//ST: !
+
+This approach is interesting (implemented for example in the software http://www.vosviewer.com[Vosviewer]), but it has drawbacks:
+
+- you need to detect adjectives and nouns in your text. This is language dependent (French put adjectives after nouns, for instance), and the processing is slow for large corpora.
+
+- what about verbs, and noun phrases comprising non adjectives, such as "United States *of* America"? These are not going to be included in the network.
+
+//ST: !
+[start=3]
+==== 3. Stemming and lemmatization
+//ST: !
+
+`live`, `lives`, `lived`: in a semantic network, it is probably useless to have 3 nodes, one for each of these 3 forms of the same root.
+
+- Stemming consists in chopping the end of the words, so that here, we would have only `live`.
+- Lemmatization is the same, but in a more subtle way: it takes grammar into account. So, "good" and better" would be reduced to "good" because there is the same basic semantic unit behind these two words, even if their lettering differ completely.
+
+//ST: !
+
+A tool performing lemmatization is https://textgrid.de/en/[TextGrid].
+It has many functions for textual analysis, and lemmatization https://wiki.de.dariah.eu/display/TextGrid/The+Lemmatizer+Tool[is explained there].
+
+== Should we represent all terms in a semantic network?
+//ST: Should we represent all terms in a semantic network?
+
+//ST: !
+We have seen that some words are more interesting than others in a corpus:
+
+- stopwords should be removed,
+- some varieties of words (`lived`, `lives`) could be grouped together (`live`).
+- sequences of words (`baby phone`) can be added because they mean more than their words taken separately (`baby`, `phone`)
+
+//ST: !
+Once this is done, we have transformed the text into plenty of words to represent. Should they all be included in the network?
+
+Imagine we have a word appearing just once, in a single footnote of a text long of 2,000 pages.
+Should this word appear? Probably not.
+
+Which rule to apply to keep or leave out a word?
+
+//ST: !
+==== 1. Start with: how many words can fit in your visualization?
+//ST: !
+
+A starting point can be the number of words you would like to see on a visualization. *A ball park figure is 300 words max*:
+
+- it already fills in all the space of a computer screen.
+- 300 words provides enough information to allow micro-topics of a text to be distinguished
+
+//ST: !
+
+More words can be crammed in a visualization, but in this case the viewer would have to take time zooming in and out, panning to explore the visualization.
+The viewer transforms into an analyst, instead of a regular reader.
+
+//ST: !
+==== 2. Representing only the most frequent terms
+//ST: !
+
+If ~ 300 words would fit in the visualization of the network, and the text you start with contains 5,000 different words: which 300 words should be selected?
+
+To visualize the semantic network *for a long, single text* the straightforward approach consists in picking the 300 most frequent words (or n-grams, see above).
+
+In the case of a collection of texts to visualize (several documents instead of one), two possibilities:
+
+//ST: !
+
+1. Either you also take the most frequent terms across these documents, like before
+
+2. Or you can apply a more subtle rule called "tf-idf", detailed below.
+
+//ST: tf-idf
+
+The idea with tf-idf is that terms which appear in all documents are not interesting, because they are so ubiquitous.
+
+Example: you retrieve all the webpages mentioning the word `Gephi`, and then want to visualize the semantic network of the texts contained in these webpages.
+
+//ST: !
+
+-> by definition, all these webpages will mention Gephi, so Gephi will probably be the most frequent term.
+
+-> so your network will end up with a node "Gephi" connected to many other terms, but you actually knew that. Boring.
+
+-> terms used in all web pages are less interesting to you than terms which are used frequently, but not uniformly accross webpages.
+
+//ST: !
+
+Applying the tf-idf correction will highlight terms which are frequently used within some texts, but not used in many texts.
+
+(to go further, here is a webpage giving a simple example: http://www.tfidf.com/)
+
+//ST: !
+So, should you visualize the most frequent words in your corpus, or the words which rank highest according to tf-idf?
+
+Both are interesting, as they show a different info. I'd suggest that the simple frequency count is easier to interpret.
+
+tf-idf can be left for specialists of the textual data under consideration, after they have been presented with the simple frequency count version.
+
+== Computing connections (edges) in the network
+//ST: Computing connections (edges) in the network
+
+//ST: !
+We now have extracted the most interesting / meaningful terms from the text.
+How to decide which connections make sense between them?
+
+//ST: !
+==== 1. Co-occurrences
+//ST: !
+
+Connections between terms are usually drawn from co-occurrences: two terms will be connected if they appear next to each other in some pre-defined unit of text:
+
+- in the same sentence
+- in the same paragraph
+- in the same document (if the corpus is made of several documents)
+
+(note on vocabulary: in the following, we will call this a "unit of text").
+
+//ST: !
+For example, in bibliometrics (the study of the publications produced by scientists), this could give:
+
+- collect *abstracts* (short summaries) of all scientific articles discussing "nano-technologies".
+- so, abstracts are our units of text here.
+- two terms will be connected if they frequently appear *in the same abstracts*.
+
+//ST: !
+==== 2. What "weight" for the edges?
+//ST: !
+
+An edge between two terms will have:
+
+- weight of "1" if these two terms co-occur in just one unit of text.
+- weight of "2" if they co-occur in two units of text.
+- etc...
+
+The logic is simple, and yet there are some refinements to discuss. It will be up to you to decide what's preferable:
+
+//ST: !
+===== If 2 terms appear several times *in a given unit of text*, should their co-occurences be counted several times?
+//ST: !
+
+An example to clarify. Let's imagine that we are interested in webpages discussing nanotechnology.
+We want to draw the semantic network of the vocabulary used in these web pages.
+
+A co-occurrence is: when 2 terms are used on the same web page.
+
+Among the pages we collected, there is the Wikipedia page discussing nanotechnology:
+
+//ST: !
+
+[quote, 'https://en.wikipedia.org/wiki/Nanotechnology[Wikipedia]']
+____
+[red]#Nanotechnology# ("nanotech") is manipulation of matter on an atomic, [blue]#molecular#, and supramolecular scale.
+The earliest, widespread description of [red]#nanotechnology# referred to the particular technological goal of precisely manipulating atoms and molecules for fabrication of macroscale products, also now referred to as [blue]#molecular# [red]#nanotechnology#
+____
+
+//ST: !
+The question is:
+
+- should I count only *one* co-occurrence between `molecular` and `nanotechnology`, because it happened on this one web page?
+- or should I consider that `molecular` appears twice on this page, and `nanotechnology` three times, so *multiple* co-occurrences between these 2 terms should be counted, just on this page already?
+
+There is no exact response, and you can experiment with both possibilities.
+
+//ST: !
+===== If two terms are very frequent, is their co-occurrence really of interest?
+//ST: !
+
+Example:
+
+Chun-Yuen Teng, Yu-Ru Lin and Lada Adamic have studied (using Gephi!) https://arxiv.org/abs/1111.3919[the pairing of ingredients in cooking recipes].
+
+So, in their study the unit of text was the "recipe", and the terms in the semantic network where ingredients in all these recipes.
+
+//ST: !
+Just because they are so common, some ingredients (like `flour`, `sugar`, `salt`) are bound to appear more frequently in the same recipes (to co-occur), than infrequent ingredients.
+
+The authors of this study chose to highlight *complementary ingredients*: some ingredients appear often used together in the same recipes, _even if they are ingredients which are quite rarely used_.
+
+"Complementary" here means that these ingredients have some interesting relationship: when one is used, the other "must" be used as well.
+
+//ST: !
+
+If we just count co-occurrences, this special relationship between infrequent complementary ingredients will be lost: by definition, 2 infrequent ingredients can't co-occurr often.
+
+To fix this, a solution consists in comparing how many times the 2 ingredients co-occur, with how frequent they are in all recipes:
+
+-> ingredients co-occurring _each and every time they are used_ will have a large edge weight,
+
+-> ingredients co-occuring many times, _but also appearing many times in different recipes_, will get a low edge weight.
+
+//ST: !
+
+A simple formula does this operation. For ingredients A and B:
+
+ weight of edge between A and B =
+ nb of recipes where A & B co-occur
+ divided by
+ (total nb of recipes where A appear x total nb of recipes where B appear)
+
+//ST: !
+
+Logs are often added to this formula, which is called "Pointwise mutual information":
+
+[asciimath]
+++++
+PMI = log((p(A, B)) /(p(A) p(B)))
+++++
+
+//ST: !
+We now have nodes and their relations: a semantic network. Let's see now how to visualize it in Gephi.
+
+
+== Visualizing semantic networks with Gephi
+//ST: Visualizing semantic networks with Gephi
+//ST: !
+
+
+
+== (to be continued)
+//ST: (to be continued)
+
+
+== More tutorials on working with semantic networks
+//ST: More tutorials on working with semantic networks
+//ST: !
+
+
+== the end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/subdir/working-with-text-en_temp_pdf.md b/docs/generated-pdf/subdir/working-with-text-en_temp_pdf.md
new file mode 100644
index 0000000..cf2726e
--- /dev/null
+++ b/docs/generated-pdf/subdir/working-with-text-en_temp_pdf.md
@@ -0,0 +1,361 @@
+= Working with text in Gephi
+Clément Levallois
+2017-03-07
+
+last modified: {docdate}
+
+:icons!:
+:asciimath:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+:sourcedir: ../../../main/java
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+{nbsp} +
+
+//ST: 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Presentation of this tutorial
+//ST: Presentation of this tutorial
+
+//ST: !
+This tutorial explains how to draw "semantic networks" like this one:
+
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="a semantic network"]
+{nbsp} +
+
+//ST: !
+
+We call "semantic network" a visualization where textual items (words, expressions) are connected to each others, like above.
+
+We will see in turn:
+
+//ST: !
+
+- why are semantic networks interesting
+- how to create a semantic network
+- tips and tricks to visualize semantic networks in the best possible way in Gephi
+
+
+== Why semantic networks?
+//ST: Why semantic networks?
+//ST: !
+
+A text, or many texts, can be hard to summarize.
+
+Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" they form.
+
+//ST: !
+
+Often, a cluster of terms characterizes a topic.
+Hence, converting a text into a semantic network helps detecting topics in the text, from micro-topics to the general themes discussed in the documents.
+
+//ST: !
+
+Semantic networks are regular networks, where:
+
+- nodes are words ("USA") or groups of words ("United States of America")
+
+- relations are, usually, signifying co-occurrences: two words are connected if they appear in the same document, or in the same paragraph, or same sentence... you decide.
+
+//ST: !
+
+It means that if you have a textual network, you can visualize it with Gephi just like any other network.
+
+Yet, not everything is the same, and this tutorial provides tips and tricks on why textual data can be a bit different than other data.
+
+== Choosing what a "term" is in a semantic network
+//ST: Choosing what a "term" is in a semantic network
+//ST: !
+
+The starting point can be: a term is a single word. So in this sentence, we would have 7 terms:
+
+ My sister lives in the United States (7 words -> 7 terms)
+
+This means that each single term is a meaningful semantic unit.
+
+This approach is simple but not great. Look again at the sentence:
+
+//ST: !
+
+ My sister lives in the United States
+
+1. `My`, `in`, `the` are frequent terms which have no special significance: they should probably be discarded
+2. `United` and `States` are meaningful separately, but here they should probably be considered together: `United States`
+3. `lives` is the conjugated form of the verb `to live`. In a network, it would make sense to regroup `live`, `lives` and `lived` as one single node.
+
+Analysts, facing each of these issues, have imagined several solutions:
+
+//ST: !
+==== 1. Removing "stopwords"
+//ST: !
+
+To remove these little terms without informational value, the most basic approach is to keep a list of them, and remove any word from the text which belongs to this list.
+
+You can find a list of these useless terms in many languages, called "stopwords", http://www.ranks.nl/stopwords/[on this website].
+
+//ST: !
+[start=2]
+==== 2. Considering "n-grams"
+//ST: !
+
+So, `United States` should probably be a meaningful unit, not just `United` and `States`.
+Because `United States` is composed of 2 terms, it is called a "bi-gram".
+
+Trigrams are interesting as well obviously (eg, `chocolate ice cream`).
+
+//ST: !
+
+People often stop there, but quadrigrams can be meaningful as well, if less frequent: `United States of America`, `functional magnetic resonance imaging`, `The New York Times`, etc.
+
+Many tools exist to extract n-grams from texts, for example http://homepages.inf.ed.ac.uk/lzhang10/ngram.html[these programs which are under a free license].
+
+//ST: !
+[start=2]
+==== 2 bis. Considering "noun phrases"
+//ST: !
+
+Another approach to go beyond single word terms (`United`, `States`) takes a different approach than n-grams. It says:
+
+ "delete all in the text except for groups of words made of nouns and adjectives, ending by a noun"
+
+-> (these are called, a bit improperly, "noun phrases")
+
+Take `United States`: it is a noun (`States`) preceded by an adjective (`United`). It will be considered as a valid term.
+
+//ST: !
+
+This approach is interesting (implemented for example in the software http://www.vosviewer.com[Vosviewer]), but it has drawbacks:
+
+- you need to detect adjectives and nouns in your text. This is language dependent (French put adjectives after nouns, for instance), and the processing is slow for large corpora.
+
+- what about verbs, and noun phrases comprising non adjectives, such as "United States *of* America"? These are not going to be included in the network.
+
+//ST: !
+[start=3]
+==== 3. Stemming and lemmatization
+//ST: !
+
+`live`, `lives`, `lived`: in a semantic network, it is probably useless to have 3 nodes, one for each of these 3 forms of the same root.
+
+- Stemming consists in chopping the end of the words, so that here, we would have only `live`.
+- Lemmatization is the same, but in a more subtle way: it takes grammar into account. So, "good" and better" would be reduced to "good" because there is the same basic semantic unit behind these two words, even if their lettering differ completely.
+
+//ST: !
+
+A tool performing lemmatization is https://textgrid.de/en/[TextGrid].
+It has many functions for textual analysis, and lemmatization https://wiki.de.dariah.eu/display/TextGrid/The+Lemmatizer+Tool[is explained there].
+
+== Should we represent all terms in a semantic network?
+//ST: Should we represent all terms in a semantic network?
+
+//ST: !
+We have seen that some words are more interesting than others in a corpus:
+
+- stopwords should be removed,
+- some varieties of words (`lived`, `lives`) could be grouped together (`live`).
+- sequences of words (`baby phone`) can be added because they mean more than their words taken separately (`baby`, `phone`)
+
+//ST: !
+Once this is done, we have transformed the text into plenty of words to represent. Should they all be included in the network?
+
+Imagine we have a word appearing just once, in a single footnote of a text long of 2,000 pages.
+Should this word appear? Probably not.
+
+Which rule to apply to keep or leave out a word?
+
+//ST: !
+==== 1. Start with: how many words can fit in your visualization?
+//ST: !
+
+A starting point can be the number of words you would like to see on a visualization. *A ball park figure is 300 words max*:
+
+- it already fills in all the space of a computer screen.
+- 300 words provides enough information to allow micro-topics of a text to be distinguished
+
+//ST: !
+
+More words can be crammed in a visualization, but in this case the viewer would have to take time zooming in and out, panning to explore the visualization.
+The viewer transforms into an analyst, instead of a regular reader.
+
+//ST: !
+==== 2. Representing only the most frequent terms
+//ST: !
+
+If ~ 300 words would fit in the visualization of the network, and the text you start with contains 5,000 different words: which 300 words should be selected?
+
+To visualize the semantic network *for a long, single text* the straightforward approach consists in picking the 300 most frequent words (or n-grams, see above).
+
+In the case of a collection of texts to visualize (several documents instead of one), two possibilities:
+
+//ST: !
+
+1. Either you also take the most frequent terms across these documents, like before
+
+2. Or you can apply a more subtle rule called "tf-idf", detailed below.
+
+//ST: tf-idf
+
+The idea with tf-idf is that terms which appear in all documents are not interesting, because they are so ubiquitous.
+
+Example: you retrieve all the webpages mentioning the word `Gephi`, and then want to visualize the semantic network of the texts contained in these webpages.
+
+//ST: !
+
+-> by definition, all these webpages will mention Gephi, so Gephi will probably be the most frequent term.
+
+-> so your network will end up with a node "Gephi" connected to many other terms, but you actually knew that. Boring.
+
+-> terms used in all web pages are less interesting to you than terms which are used frequently, but not uniformly accross webpages.
+
+//ST: !
+
+Applying the tf-idf correction will highlight terms which are frequently used within some texts, but not used in many texts.
+
+(to go further, here is a webpage giving a simple example: http://www.tfidf.com/)
+
+//ST: !
+So, should you visualize the most frequent words in your corpus, or the words which rank highest according to tf-idf?
+
+Both are interesting, as they show a different info. I'd suggest that the simple frequency count is easier to interpret.
+
+tf-idf can be left for specialists of the textual data under consideration, after they have been presented with the simple frequency count version.
+
+== Computing connections (edges) in the network
+//ST: Computing connections (edges) in the network
+
+//ST: !
+We now have extracted the most interesting / meaningful terms from the text.
+How to decide which connections make sense between them?
+
+//ST: !
+==== 1. Co-occurrences
+//ST: !
+
+Connections between terms are usually drawn from co-occurrences: two terms will be connected if they appear next to each other in some pre-defined unit of text:
+
+- in the same sentence
+- in the same paragraph
+- in the same document (if the corpus is made of several documents)
+
+(note on vocabulary: in the following, we will call this a "unit of text").
+
+//ST: !
+For example, in bibliometrics (the study of the publications produced by scientists), this could give:
+
+- collect *abstracts* (short summaries) of all scientific articles discussing "nano-technologies".
+- so, abstracts are our units of text here.
+- two terms will be connected if they frequently appear *in the same abstracts*.
+
+//ST: !
+==== 2. What "weight" for the edges?
+//ST: !
+
+An edge between two terms will have:
+
+- weight of "1" if these two terms co-occur in just one unit of text.
+- weight of "2" if they co-occur in two units of text.
+- etc...
+
+The logic is simple, and yet there are some refinements to discuss. It will be up to you to decide what's preferable:
+
+//ST: !
+===== If 2 terms appear several times *in a given unit of text*, should their co-occurences be counted several times?
+//ST: !
+
+An example to clarify. Let's imagine that we are interested in webpages discussing nanotechnology.
+We want to draw the semantic network of the vocabulary used in these web pages.
+
+A co-occurrence is: when 2 terms are used on the same web page.
+
+Among the pages we collected, there is the Wikipedia page discussing nanotechnology:
+
+//ST: !
+
+[quote, 'https://en.wikipedia.org/wiki/Nanotechnology[Wikipedia]']
+____
+[red]#Nanotechnology# ("nanotech") is manipulation of matter on an atomic, [blue]#molecular#, and supramolecular scale.
+The earliest, widespread description of [red]#nanotechnology# referred to the particular technological goal of precisely manipulating atoms and molecules for fabrication of macroscale products, also now referred to as [blue]#molecular# [red]#nanotechnology#
+____
+
+//ST: !
+The question is:
+
+- should I count only *one* co-occurrence between `molecular` and `nanotechnology`, because it happened on this one web page?
+- or should I consider that `molecular` appears twice on this page, and `nanotechnology` three times, so *multiple* co-occurrences between these 2 terms should be counted, just on this page already?
+
+There is no exact response, and you can experiment with both possibilities.
+
+//ST: !
+===== If two terms are very frequent, is their co-occurrence really of interest?
+//ST: !
+
+Example:
+
+Chun-Yuen Teng, Yu-Ru Lin and Lada Adamic have studied (using Gephi!) https://arxiv.org/abs/1111.3919[the pairing of ingredients in cooking recipes].
+
+So, in their study the unit of text was the "recipe", and the terms in the semantic network where ingredients in all these recipes.
+
+//ST: !
+Just because they are so common, some ingredients (like `flour`, `sugar`, `salt`) are bound to appear more frequently in the same recipes (to co-occur), than infrequent ingredients.
+
+The authors of this study chose to highlight *complementary ingredients*: some ingredients appear often used together in the same recipes, _even if they are ingredients which are quite rarely used_.
+
+"Complementary" here means that these ingredients have some interesting relationship: when one is used, the other "must" be used as well.
+
+//ST: !
+
+If we just count co-occurrences, this special relationship between infrequent complementary ingredients will be lost: by definition, 2 infrequent ingredients can't co-occurr often.
+
+To fix this, a solution consists in comparing how many times the 2 ingredients co-occur, with how frequent they are in all recipes:
+
+-> ingredients co-occurring _each and every time they are used_ will have a large edge weight,
+
+-> ingredients co-occuring many times, _but also appearing many times in different recipes_, will get a low edge weight.
+
+//ST: !
+
+A simple formula does this operation. For ingredients A and B:
+
+ weight of edge between A and B =
+ nb of recipes where A & B co-occur
+ divided by
+ (total nb of recipes where A appear x total nb of recipes where B appear)
+
+//ST: !
+
+Logs are often added to this formula, which is called "Pointwise mutual information":
+
+[asciimath]
+++++
+PMI = log((p(A, B)) /(p(A) p(B)))
+++++
+
+//ST: !
+We now have nodes and their relations: a semantic network. Let's see now how to visualize it in Gephi.
+
+
+== Visualizing semantic networks with Gephi
+//ST: Visualizing semantic networks with Gephi
+//ST: !
+
+
+
+== (to be continued)
+//ST: (to be continued)
+
+
+== More tutorials on working with semantic networks
+//ST: More tutorials on working with semantic networks
+//ST: !
+
+
+== the end
+//ST: The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
diff --git a/docs/generated-pdf/subdir/working-with-text-en_temp_slides.md b/docs/generated-pdf/subdir/working-with-text-en_temp_slides.md
new file mode 100644
index 0000000..0166a96
--- /dev/null
+++ b/docs/generated-pdf/subdir/working-with-text-en_temp_slides.md
@@ -0,0 +1,370 @@
+= Working with text in Gephi
+Clément Levallois
+2017-03-07
+
+last modified: {docdate}
+
+:icons!:
+:asciimath:
+:iconsfont: font-awesome
+:revnumber: 1.0
+:example-caption!:
+
+:title-logo-image: gephi-logo-2010-transparent.png[width="450" align="center"]
+
+[.stretch]
+image::gephi-logo-2010-transparent.png[width="450" align="center"]
+
+
+== 'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
+
+== Presentation of this tutorial
+
+== !
+This tutorial explains how to draw "semantic networks" like this one:
+
+[.stretch]
+image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="a semantic network"]
+
+
+== !
+
+We call "semantic network" a visualization where textual items (words, expressions) are connected to each others, like above.
+
+We will see in turn:
+
+== !
+
+- why are semantic networks interesting
+- how to create a semantic network
+- tips and tricks to visualize semantic networks in the best possible way in Gephi
+
+
+== Why semantic networks?
+== !
+
+A text, or many texts, can be hard to summarize.
+
+Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" they form.
+
+== !
+
+Often, a cluster of terms characterizes a topic.
+Hence, converting a text into a semantic network helps detecting topics in the text, from micro-topics to the general themes discussed in the documents.
+
+== !
+
+Semantic networks are regular networks, where:
+
+- nodes are words ("USA") or groups of words ("United States of America")
+
+- relations are, usually, signifying co-occurrences: two words are connected if they appear in the same document, or in the same paragraph, or same sentence... you decide.
+
+== !
+
+It means that if you have a textual network, you can visualize it with Gephi just like any other network.
+
+Yet, not everything is the same, and this tutorial provides tips and tricks on why textual data can be a bit different than other data.
+
+== Choosing what a "term" is in a semantic network
+== !
+
+The starting point can be: a term is a single word. So in this sentence, we would have 7 terms:
+
+ My sister lives in the United States (7 words -> 7 terms)
+
+This means that each single term is a meaningful semantic unit.
+
+This approach is simple but not great. Look again at the sentence:
+
+== !
+
+ My sister lives in the United States
+
+1. `My`, `in`, `the` are frequent terms which have no special significance: they should probably be discarded
+2. `United` and `States` are meaningful separately, but here they should probably be considered together: `United States`
+3. `lives` is the conjugated form of the verb `to live`. In a network, it would make sense to regroup `live`, `lives` and `lived` as one single node.
+
+Analysts, facing each of these issues, have imagined several solutions:
+
+== !
+==== 1. Removing "stopwords"
+== !
+
+To remove these little terms without informational value, the most basic approach is to keep a list of them, and remove any word from the text which belongs to this list.
+
+You can find a list of these useless terms in many languages, called "stopwords", http://www.ranks.nl/stopwords/[on this website].
+
+== !
+[start=2]
+==== 2. Considering "n-grams"
+== !
+
+So, `United States` should probably be a meaningful unit, not just `United` and `States`.
+Because `United States` is composed of 2 terms, it is called a "bi-gram".
+
+Trigrams are interesting as well obviously (eg, `chocolate ice cream`).
+
+== !
+
+People often stop there, but quadrigrams can be meaningful as well, if less frequent: `United States of America`, `functional magnetic resonance imaging`, `The New York Times`, etc.
+
+Many tools exist to extract n-grams from texts, for example http://homepages.inf.ed.ac.uk/lzhang10/ngram.html[these programs which are under a free license].
+
+== !
+[start=2]
+==== 2 bis. Considering "noun phrases"
+== !
+
+Another approach to go beyond single word terms (`United`, `States`) takes a different approach than n-grams. It says:
+
+ "delete all in the text except for groups of words made of nouns and adjectives, ending by a noun"
+
+-> (these are called, a bit improperly, "noun phrases")
+
+Take `United States`: it is a noun (`States`) preceded by an adjective (`United`). It will be considered as a valid term.
+
+== !
+
+This approach is interesting (implemented for example in the software http://www.vosviewer.com[Vosviewer]), but it has drawbacks:
+
+- you need to detect adjectives and nouns in your text. This is language dependent (French put adjectives after nouns, for instance), and the processing is slow for large corpora.
+
+- what about verbs, and noun phrases comprising non adjectives, such as "United States *of* America"? These are not going to be included in the network.
+
+== !
+[start=3]
+==== 3. Stemming and lemmatization
+== !
+
+`live`, `lives`, `lived`: in a semantic network, it is probably useless to have 3 nodes, one for each of these 3 forms of the same root.
+
+- Stemming consists in chopping the end of the words, so that here, we would have only `live`.
+- Lemmatization is the same, but in a more subtle way: it takes grammar into account. So, "good" and better" would be reduced to "good" because there is the same basic semantic unit behind these two words, even if their lettering differ completely.
+
+== !
+
+A tool performing lemmatization is https://textgrid.de/en/[TextGrid].
+It has many functions for textual analysis, and lemmatization https://wiki.de.dariah.eu/display/TextGrid/The+Lemmatizer+Tool[is explained there].
+
+== Should we represent all terms in a semantic network?
+
+== !
+We have seen that some words are more interesting than others in a corpus:
+
+- stopwords should be removed,
+- some varieties of words (`lived`, `lives`) could be grouped together (`live`).
+- sequences of words (`baby phone`) can be added because they mean more than their words taken separately (`baby`, `phone`)
+
+== !
+Once this is done, we have transformed the text into plenty of words to represent. Should they all be included in the network?
+
+Imagine we have a word appearing just once, in a single footnote of a text long of 2,000 pages.
+Should this word appear? Probably not.
+
+Which rule to apply to keep or leave out a word?
+
+== !
+==== 1. Start with: how many words can fit in your visualization?
+== !
+
+A starting point can be the number of words you would like to see on a visualization. *A ball park figure is 300 words max*:
+
+- it already fills in all the space of a computer screen.
+- 300 words provides enough information to allow micro-topics of a text to be distinguished
+
+== !
+
+More words can be crammed in a visualization, but in this case the viewer would have to take time zooming in and out, panning to explore the visualization.
+The viewer transforms into an analyst, instead of a regular reader.
+
+== !
+==== 2. Representing only the most frequent terms
+== !
+
+If ~ 300 words would fit in the visualization of the network, and the text you start with contains 5,000 different words: which 300 words should be selected?
+
+To visualize the semantic network *for a long, single text* the straightforward approach consists in picking the 300 most frequent words (or n-grams, see above).
+
+In the case of a collection of texts to visualize (several documents instead of one), two possibilities:
+
+== !
+
+1. Either you also take the most frequent terms across these documents, like before
+
+2. Or you can apply a more subtle rule called "tf-idf", detailed below.
+
+== tf-idf
+
+The idea with tf-idf is that terms which appear in all documents are not interesting, because they are so ubiquitous.
+
+Example: you retrieve all the webpages mentioning the word `Gephi`, and then want to visualize the semantic network of the texts contained in these webpages.
+
+== !
+
+-> by definition, all these webpages will mention Gephi, so Gephi will probably be the most frequent term.
+
+-> so your network will end up with a node "Gephi" connected to many other terms, but you actually knew that. Boring.
+
+-> terms used in all web pages are less interesting to you than terms which are used frequently, but not uniformly accross webpages.
+
+== !
+
+Applying the tf-idf correction will highlight terms which are frequently used within some texts, but not used in many texts.
+
+(to go further, here is a webpage giving a simple example: http://www.tfidf.com/)
+
+== !
+So, should you visualize the most frequent words in your corpus, or the words which rank highest according to tf-idf?
+
+Both are interesting, as they show a different info. I'd suggest that the simple frequency count is easier to interpret.
+
+tf-idf can be left for specialists of the textual data under consideration, after they have been presented with the simple frequency count version.
+
+== Computing connections (edges) in the network
+
+== !
+We now have extracted the most interesting / meaningful terms from the text.
+How to decide which connections make sense between them?
+
+== !
+==== 1. Co-occurrences
+== !
+
+Connections between terms are usually drawn from co-occurrences: two terms will be connected if they appear next to each other in some pre-defined unit of text:
+
+- in the same sentence
+- in the same paragraph
+- in the same document (if the corpus is made of several documents)
+
+(note on vocabulary: in the following, we will call this a "unit of text").
+
+== !
+For example, in bibliometrics (the study of the publications produced by scientists), this could give:
+
+- collect *abstracts* (short summaries) of all scientific articles discussing "nano-technologies".
+- so, abstracts are our units of text here.
+- two terms will be connected if they frequently appear *in the same abstracts*.
+
+== !
+==== 2. What "weight" for the edges?
+== !
+
+An edge between two terms will have:
+
+- weight of "1" if these two terms co-occur in just one unit of text.
+- weight of "2" if they co-occur in two units of text.
+- etc...
+
+The logic is simple, and yet there are some refinements to discuss. It will be up to you to decide what's preferable:
+
+== !
+===== If 2 terms appear several times *in a given unit of text*, should their co-occurences be counted several times?
+== !
+
+An example to clarify. Let's imagine that we are interested in webpages discussing nanotechnology.
+We want to draw the semantic network of the vocabulary used in these web pages.
+
+A co-occurrence is: when 2 terms are used on the same web page.
+
+Among the pages we collected, there is the Wikipedia page discussing nanotechnology:
+
+== !
+
+[quote, 'https://en.wikipedia.org/wiki/Nanotechnology[Wikipedia]']
+____
+[red]#Nanotechnology# ("nanotech") is manipulation of matter on an atomic, [blue]#molecular#, and supramolecular scale.
+The earliest, widespread description of [red]#nanotechnology# referred to the particular technological goal of precisely manipulating atoms and molecules for fabrication of macroscale products, also now referred to as [blue]#molecular# [red]#nanotechnology#
+____
+
+== !
+The question is:
+
+- should I count only *one* co-occurrence between `molecular` and `nanotechnology`, because it happened on this one web page?
+- or should I consider that `molecular` appears twice on this page, and `nanotechnology` three times, so *multiple* co-occurrences between these 2 terms should be counted, just on this page already?
+
+There is no exact response, and you can experiment with both possibilities.
+
+== !
+===== If two terms are very frequent, is their co-occurrence really of interest?
+== !
+
+Example:
+
+Chun-Yuen Teng, Yu-Ru Lin and Lada Adamic have studied (using Gephi!) https://arxiv.org/abs/1111.3919[the pairing of ingredients in cooking recipes].
+
+So, in their study the unit of text was the "recipe", and the terms in the semantic network where ingredients in all these recipes.
+
+== !
+Just because they are so common, some ingredients (like `flour`, `sugar`, `salt`) are bound to appear more frequently in the same recipes (to co-occur), than infrequent ingredients.
+
+The authors of this study chose to highlight *complementary ingredients*: some ingredients appear often used together in the same recipes, _even if they are ingredients which are quite rarely used_.
+
+"Complementary" here means that these ingredients have some interesting relationship: when one is used, the other "must" be used as well.
+
+== !
+
+If we just count co-occurrences, this special relationship between infrequent complementary ingredients will be lost: by definition, 2 infrequent ingredients can't co-occurr often.
+
+To fix this, a solution consists in comparing how many times the 2 ingredients co-occur, with how frequent they are in all recipes:
+
+-> ingredients co-occurring _each and every time they are used_ will have a large edge weight,
+
+-> ingredients co-occuring many times, _but also appearing many times in different recipes_, will get a low edge weight.
+
+== !
+
+A simple formula does this operation. For ingredients A and B:
+
+ weight of edge between A and B =
+ nb of recipes where A & B co-occur
+ divided by
+ (total nb of recipes where A appear x total nb of recipes where B appear)
+
+== !
+
+Logs are often added to this formula, which is called "Pointwise mutual information":
+
+[asciimath]
+++++
+PMI = log((p(A, B)) /(p(A) p(B)))
+++++
+
+== !
+We now have nodes and their relations: a semantic network. Let's see now how to visualize it in Gephi.
+
+
+== Visualizing semantic networks with Gephi
+== !
+
+
+
+== (to be continued)
+
+
+== More tutorials on working with semantic networks
+== !
+
+
+== The end!
+
+Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get help,
+
+or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
+pass:[
+
+
+ ]
diff --git a/docs/generated-pdf/working-with-text-en.pdf b/docs/generated-pdf/working-with-text-en.pdf
index d465bc7..77b30cc 100644
Binary files a/docs/generated-pdf/working-with-text-en.pdf and b/docs/generated-pdf/working-with-text-en.pdf differ
diff --git a/docs/generated-slides/subdir/working-with-text-en_temp_common.md b/docs/generated-slides/subdir/working-with-text-en_temp_common.md
index e8e3df2..e28e6a9 100644
--- a/docs/generated-slides/subdir/working-with-text-en_temp_common.md
+++ b/docs/generated-slides/subdir/working-with-text-en_temp_common.md
@@ -5,6 +5,7 @@ Clément Levallois
last modified: {docdate}
:icons!:
+:asciimath:
:iconsfont: font-awesome
:revnumber: 1.0
:example-caption!:
@@ -44,7 +45,7 @@ We will see in turn:
A text, or many texts, can be hard to summarize.
-Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" of they form.
+Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" they form.
//ST: !
@@ -57,7 +58,7 @@ Semantic networks are regular networks, where:
- nodes are words ("USA") or groups of words ("United States of America")
-- relations are, usually, signifying a co-occurrences: two words are connected if they co-occur.
+- relations are, usually, signifying co-occurrences: two words are connected if they appear in the same document, or in the same paragraph, or same sentence... you decide.
//ST: !
@@ -118,7 +119,7 @@ Many tools exist to extract n-grams from texts, for example http://homepages.inf
Another approach to go beyond single word terms (`United`, `States`) takes a different approach than n-grams. It says:
- "delete all in the text except for all groups of words made of nouns and adjectives, ending by a noun"
+ "delete all in the text except for groups of words made of nouns and adjectives, ending by a noun"
-> (these are called, a bit improperly, "noun phrases")
@@ -147,7 +148,6 @@ This approach is interesting (implemented for example in the software http://www
A tool performing lemmatization is https://textgrid.de/en/[TextGrid].
It has many functions for textual analysis, and lemmatization https://wiki.de.dariah.eu/display/TextGrid/The+Lemmatizer+Tool[is explained there].
-
== Should we represent all terms in a semantic network?
//ST: Should we represent all terms in a semantic network?
@@ -223,6 +223,126 @@ Both are interesting, as they show a different info. I'd suggest that the simple
tf-idf can be left for specialists of the textual data under consideration, after they have been presented with the simple frequency count version.
+== Computing connections (edges) in the network
+//ST: Computing connections (edges) in the network
+
+//ST: !
+We now have extracted the most interesting / meaningful terms from the text.
+How to decide which connections make sense between them?
+
+//ST: !
+==== 1. Co-occurrences
+//ST: !
+
+Connections between terms are usually drawn from co-occurrences: two terms will be connected if they appear next to each other in some pre-defined unit of text:
+
+- in the same sentence
+- in the same paragraph
+- in the same document (if the corpus is made of several documents)
+
+(note on vocabulary: in the following, we will call this a "unit of text").
+
+//ST: !
+For example, in bibliometrics (the study of the publications produced by scientists), this could give:
+
+- collect *abstracts* (short summaries) of all scientific articles discussing "nano-technologies".
+- so, abstracts are our units of text here.
+- two terms will be connected if they frequently appear *in the same abstracts*.
+
+//ST: !
+==== 2. What "weight" for the edges?
+//ST: !
+
+An edge between two terms will have:
+
+- weight of "1" if these two terms co-occur in just one unit of text.
+- weight of "2" if they co-occur in two units of text.
+- etc...
+
+The logic is simple, and yet there are some refinements to discuss. It will be up to you to decide what's preferable:
+
+//ST: !
+===== If 2 terms appear several times *in a given unit of text*, should their co-occurences be counted several times?
+//ST: !
+
+An example to clarify. Let's imagine that we are interested in webpages discussing nanotechnology.
+We want to draw the semantic network of the vocabulary used in these web pages.
+
+A co-occurrence is: when 2 terms are used on the same web page.
+
+Among the pages we collected, there is the Wikipedia page discussing nanotechnology:
+
+//ST: !
+
+[quote, 'https://en.wikipedia.org/wiki/Nanotechnology[Wikipedia]']
+____
+[red]#Nanotechnology# ("nanotech") is manipulation of matter on an atomic, [blue]#molecular#, and supramolecular scale.
+The earliest, widespread description of [red]#nanotechnology# referred to the particular technological goal of precisely manipulating atoms and molecules for fabrication of macroscale products, also now referred to as [blue]#molecular# [red]#nanotechnology#
+____
+
+//ST: !
+The question is:
+
+- should I count only *one* co-occurrence between `molecular` and `nanotechnology`, because it happened on this one web page?
+- or should I consider that `molecular` appears twice on this page, and `nanotechnology` three times, so *multiple* co-occurrences between these 2 terms should be counted, just on this page already?
+
+There is no exact response, and you can experiment with both possibilities.
+
+//ST: !
+===== If two terms are very frequent, is their co-occurrence really of interest?
+//ST: !
+
+Example:
+
+Chun-Yuen Teng, Yu-Ru Lin and Lada Adamic have studied (using Gephi!) https://arxiv.org/abs/1111.3919[the pairing of ingredients in cooking recipes].
+
+So, in their study the unit of text was the "recipe", and the terms in the semantic network where ingredients in all these recipes.
+
+//ST: !
+Just because they are so common, some ingredients (like `flour`, `sugar`, `salt`) are bound to appear more frequently in the same recipes (to co-occur), than infrequent ingredients.
+
+The authors of this study chose to highlight *complementary ingredients*: some ingredients appear often used together in the same recipes, _even if they are ingredients which are quite rarely used_.
+
+"Complementary" here means that these ingredients have some interesting relationship: when one is used, the other "must" be used as well.
+
+//ST: !
+
+If we just count co-occurrences, this special relationship between infrequent complementary ingredients will be lost: by definition, 2 infrequent ingredients can't co-occurr often.
+
+To fix this, a solution consists in comparing how many times the 2 ingredients co-occur, with how frequent they are in all recipes:
+
+-> ingredients co-occurring _each and every time they are used_ will have a large edge weight,
+
+-> ingredients co-occuring many times, _but also appearing many times in different recipes_, will get a low edge weight.
+
+//ST: !
+
+A simple formula does this operation. For ingredients A and B:
+
+ weight of edge between A and B =
+ nb of recipes where A & B co-occur
+ divided by
+ (total nb of recipes where A appear x total nb of recipes where B appear)
+
+//ST: !
+
+Logs are often added to this formula, which is called "Pointwise mutual information":
+
+[asciimath]
+++++
+PMI = log((p(A, B)) /(p(A) p(B)))
+++++
+
+//ST: !
+We now have nodes and their relations: a semantic network. Let's see now how to visualize it in Gephi.
+
+
+== Visualizing semantic networks with Gephi
+//ST: Visualizing semantic networks with Gephi
+//ST: !
+
+
+
== (to be continued)
//ST: (to be continued)
diff --git a/docs/generated-slides/subdir/working-with-text-en_temp_html.md b/docs/generated-slides/subdir/working-with-text-en_temp_html.md
index 5b12666..589b277 100644
--- a/docs/generated-slides/subdir/working-with-text-en_temp_html.md
+++ b/docs/generated-slides/subdir/working-with-text-en_temp_html.md
@@ -5,6 +5,7 @@ Clément Levallois
last modified: {docdate}
:icons!:
+:asciimath:
:iconsfont: font-awesome
:revnumber: 1.0
:example-caption!:
@@ -31,6 +32,7 @@ image::en/cooccurrences-computer/gephi-result-1-en.png[align="center", title="a
We call "semantic network" a visualization where textual items (words, expressions) are connected to each others, like above.
We will see in turn:
+
//ST: !
- why are semantic networks interesting
@@ -44,7 +46,7 @@ We will see in turn:
A text, or many texts, can be hard to summarize.
-Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" of they form.
+Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" they form.
//ST: !
@@ -57,7 +59,7 @@ Semantic networks are regular networks, where:
- nodes are words ("USA") or groups of words ("United States of America")
-- relations are, usually, signifying a co-occurrences: two words are connected if they co-occur.
+- relations are, usually, signifying co-occurrences: two words are connected if they appear in the same document, or in the same paragraph, or same sentence... you decide.
//ST: !
@@ -107,7 +109,7 @@ Trigrams are interesting as well obviously (eg, `chocolate ice cream`).
//ST: !
-People often stop there, but I find that quadrigrams can be meaningful as well, if less frequent: `United States of America`, `functional magnetic resonance imaging`, `The New York Times`, etc.
+People often stop there, but quadrigrams can be meaningful as well, if less frequent: `United States of America`, `functional magnetic resonance imaging`, `The New York Times`, etc.
Many tools exist to extract n-grams from texts, for example http://homepages.inf.ed.ac.uk/lzhang10/ngram.html[these programs which are under a free license].
@@ -118,7 +120,7 @@ Many tools exist to extract n-grams from texts, for example http://homepages.inf
Another approach to go beyond single word terms (`United`, `States`) takes a different approach than n-grams. It says:
- "delete all in the text except for all groups of words made of nouns and adjectives, ending by a noun"
+ "delete all in the text except for groups of words made of nouns and adjectives, ending by a noun"
-> (these are called, a bit improperly, "noun phrases")
@@ -147,7 +149,6 @@ This approach is interesting (implemented for example in the software http://www
A tool performing lemmatization is https://textgrid.de/en/[TextGrid].
It has many functions for textual analysis, and lemmatization https://wiki.de.dariah.eu/display/TextGrid/The+Lemmatizer+Tool[is explained there].
-
== Should we represent all terms in a semantic network?
//ST: Should we represent all terms in a semantic network?
@@ -223,6 +224,126 @@ Both are interesting, as they show a different info. I'd suggest that the simple
tf-idf can be left for specialists of the textual data under consideration, after they have been presented with the simple frequency count version.
+== Computing connections (edges) in the network
+//ST: Computing connections (edges) in the network
+
+//ST: !
+We now have extracted the most interesting / meaningful terms from the text.
+How to decide which connections make sense between them?
+
+//ST: !
+==== 1. Co-occurrences
+//ST: !
+
+Connections between terms are usually drawn from co-occurrences: two terms will be connected if they appear next to each other in some pre-defined unit of text:
+
+- in the same sentence
+- in the same paragraph
+- in the same document (if the corpus is made of several documents)
+
+(note on vocabulary: in the following, we will call this a "unit of text").
+
+//ST: !
+For example, in bibliometrics (the study of the publications produced by scientists), this could give:
+
+- collect *abstracts* (short summaries) of all scientific articles discussing "nano-technologies".
+- so, abstracts are our units of text here.
+- two terms will be connected if they frequently appear *in the same abstracts*.
+
+//ST: !
+==== 2. What "weight" for the edges?
+//ST: !
+
+An edge between two terms will have:
+
+- weight of "1" if these two terms co-occur in just one unit of text.
+- weight of "2" if they co-occur in two units of text.
+- etc...
+
+The logic is simple, and yet there are some refinements to discuss. It will be up to you to decide what's preferable:
+
+//ST: !
+===== If 2 terms appear several times *in a given unit of text*, should their co-occurences be counted several times?
+//ST: !
+
+An example to clarify. Let's imagine that we are interested in webpages discussing nanotechnology.
+We want to draw the semantic network of the vocabulary used in these web pages.
+
+A co-occurrence is: when 2 terms are used on the same web page.
+
+Among the pages we collected, there is the Wikipedia page discussing nanotechnology:
+
+//ST: !
+
+[quote, 'https://en.wikipedia.org/wiki/Nanotechnology[Wikipedia]']
+____
+[red]#Nanotechnology# ("nanotech") is manipulation of matter on an atomic, [blue]#molecular#, and supramolecular scale.
+The earliest, widespread description of [red]#nanotechnology# referred to the particular technological goal of precisely manipulating atoms and molecules for fabrication of macroscale products, also now referred to as [blue]#molecular# [red]#nanotechnology#
+____
+
+//ST: !
+The question is:
+
+- should I count only *one* co-occurrence between `molecular` and `nanotechnology`, because it happened on this one web page?
+- or should I consider that `molecular` appears twice on this page, and `nanotechnology` three times, so *multiple* co-occurrences between these 2 terms should be counted, just on this page already?
+
+There is no exact response, and you can experiment with both possibilities.
+
+//ST: !
+===== If two terms are very frequent, is their co-occurrence really of interest?
+//ST: !
+
+Example:
+
+Chun-Yuen Teng, Yu-Ru Lin and Lada Adamic have studied (using Gephi!) https://arxiv.org/abs/1111.3919[the pairing of ingredients in cooking recipes].
+
+So, in their study the unit of text was the "recipe", and the terms in the semantic network where ingredients in all these recipes.
+
+//ST: !
+Just because they are so common, some ingredients (like `flour`, `sugar`, `salt`) are bound to appear more frequently in the same recipes (to co-occur), than infrequent ingredients.
+
+The authors of this study chose to highlight *complementary ingredients*: some ingredients appear often used together in the same recipes, _even if they are ingredients which are quite rarely used_.
+
+"Complementary" here means that these ingredients have some interesting relationship: when one is used, the other "must" be used as well.
+
+//ST: !
+
+If we just count co-occurrences, this special relationship between infrequent complementary ingredients will be lost: by definition, 2 infrequent ingredients can't co-occurr often.
+
+To fix this, a solution consists in comparing how many times the 2 ingredients co-occur, with how frequent they are in all recipes:
+
+-> ingredients co-occurring _each and every time they are used_ will have a large edge weight,
+
+-> ingredients co-occuring many times, _but also appearing many times in different recipes_, will get a low edge weight.
+
+//ST: !
+
+A simple formula does this operation. For ingredients A and B:
+
+ weight of edge between A and B =
+ nb of recipes where A & B co-occur
+ divided by
+ (total nb of recipes where A appear x total nb of recipes where B appear)
+
+//ST: !
+
+Logs are often added to this formula, which is called "Pointwise mutual information":
+
+[asciimath]
+++++
+PMI = log((p(A, B)) /(p(A) p(B)))
+++++
+
+//ST: !
+We now have nodes and their relations: a semantic network. Let's see now how to visualize it in Gephi.
+
+
+== Visualizing semantic networks with Gephi
+//ST: Visualizing semantic networks with Gephi
+//ST: !
+
+
+
== (to be continued)
//ST: (to be continued)
@@ -240,9 +361,9 @@ Visit https://www.facebook.com/groups/gephi/[the Gephi group on Facebook] to get
or visit https://seinecle.github.io/gephi-tutorials/[the website for more tutorials]
pass:[
Working with text in Gephi
last modified: 2017-03-07
+.CodeRay .head .head{color:#f4f}
Working with text in Gephi
last modified: 2017-03-08
Clément Levallois
'Escape' or 'o' to see all sides, F11 for full screen, 's' for speaker notes
Presentation of this tutorial
@@ -97,11 +97,11 @@
why are semantic networks interesting
how to create a semantic network
tips and tricks to visualize semantic networks in the best possible way in Gephi
Why semantic networks?
A text, or many texts, can be hard to summarize.
-
Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" of they form.
+
Drawing a semantic network highlights what are the most frequent terms, how they relate to each other, and reveal the different groups or "clusters" they form.
Often, a cluster of terms characterizes a topic.
Hence, converting a text into a semantic network helps detecting topics in the text, from micro-topics to the general themes discussed in the documents.
Semantic networks are regular networks, where:
-
nodes are words ("USA") or groups of words ("United States of America")
relations are, usually, signifying a co-occurrences: two words are connected if they co-occur.
+
nodes are words ("USA") or groups of words ("United States of America")
relations are, usually, signifying co-occurrences: two words are connected if they appear in the same document, or in the same paragraph, or same sentence… you decide.
It means that if you have a textual network, you can visualize it with Gephi just like any other network.
Yet, not everything is the same, and this tutorial provides tips and tricks on why textual data can be a bit different than other data.
Another approach to go beyond single word terms (United, States) takes a different approach than n-grams. It says:
-
"delete all in the text except for all groups of words made of nouns and adjectives, ending by a noun"
+
"delete all in the text except for groups of words made of nouns and adjectives, ending by a noun"
→ (these are called, a bit improperly, "noun phrases")
Take United States: it is a noun (States) preceded by an adjective (United). It will be considered as a valid term.
This approach is interesting (implemented for example in the software Vosviewer), but it has drawbacks:
@@ -160,6 +160,50 @@
So, should you visualize the most frequent words in your corpus, or the words which rank highest according to tf-idf?
Both are interesting, as they show a different info. I’d suggest that the simple frequency count is easier to interpret.
tf-idf can be left for specialists of the textual data under consideration, after they have been presented with the simple frequency count version.
+
Computing connections (edges) in the network
+
We now have extracted the most interesting / meaningful terms from the text.
+How to decide which connections make sense between them?
+
1. Co-occurrences
+
Connections between terms are usually drawn from co-occurrences: two terms will be connected if they appear next to each other in some pre-defined unit of text:
+
in the same sentence
in the same paragraph
in the same document (if the corpus is made of several documents)
+
(note on vocabulary: in the following, we will call this a "unit of text").
+
For example, in bibliometrics (the study of the publications produced by scientists), this could give:
+
collect abstracts (short summaries) of all scientific articles discussing "nano-technologies".
so, abstracts are our units of text here.
two terms will be connected if they frequently appear in the same abstracts.
+
2. What "weight" for the edges?
+
An edge between two terms will have:
+
weight of "1" if these two terms co-occur in just one unit of text.
weight of "2" if they co-occur in two units of text.
etc…
+
The logic is simple, and yet there are some refinements to discuss. It will be up to you to decide what’s preferable:
+
If 2 terms appear several times in a given unit of text, should their co-occurences be counted several times?
+
An example to clarify. Let’s imagine that we are interested in webpages discussing nanotechnology.
+We want to draw the semantic network of the vocabulary used in these web pages.
+
A co-occurrence is: when 2 terms are used on the same web page.
+
Among the pages we collected, there is the Wikipedia page discussing nanotechnology:
+
Nanotechnology ("nanotech") is manipulation of matter on an atomic, molecular, and supramolecular scale.
+The earliest, widespread description of nanotechnology referred to the particular technological goal of precisely manipulating atoms and molecules for fabrication of macroscale products, also now referred to as molecularnanotechnology
should I count only one co-occurrence between molecular and nanotechnology, because it happened on this one web page?
or should I consider that molecular appears twice on this page, and nanotechnology three times, so multiple co-occurrences between these 2 terms should be counted, just on this page already?
+
There is no exact response, and you can experiment with both possibilities.
+
If two terms are very frequent, is their co-occurrence really of interest?
So, in their study the unit of text was the "recipe", and the terms in the semantic network where ingredients in all these recipes.
+
Just because they are so common, some ingredients (like flour, sugar, salt) are bound to appear more frequently in the same recipes (to co-occur), than infrequent ingredients.
+
The authors of this study chose to highlight complementary ingredients: some ingredients appear often used together in the same recipes, even if they are ingredients which are quite rarely used.
+
"Complementary" here means that these ingredients have some interesting relationship: when one is used, the other "must" be used as well.
+
If we just count co-occurrences, this special relationship between infrequent complementary ingredients will be lost: by definition, 2 infrequent ingredients can’t co-occurr often.
+
To fix this, a solution consists in comparing how many times the 2 ingredients co-occur, with how frequent they are in all recipes:
+
→ ingredients co-occurring each and every time they are used will have a large edge weight,
+
→ ingredients co-occuring many times, but also appearing many times in different recipes, will get a low edge weight.
+
A simple formula does this operation. For ingredients A and B:
+
weight of edge between A and B =
+nb of recipes where A & B co-occur
+divided by
+(total nb of recipes where A appear x total nb of recipes where B appear)
+
Logs are often added to this formula, which is called "Pointwise mutual information":
+
\$PMI = log((p(A, B)) /(p(A) p(B)))\$
+
We now have nodes and their relations: a semantic network. Let’s see now how to visualize it in Gephi.
