These datasets were derived from a Wikipedia scrape on all Academy Award-nominated films. This data consists of two separate CSVs.
This is a list of all entities (movies & actors) that appear in the original data, as well as some metadata about some of them.
| id | label | type | year | won | run_time | budget | box_office |
|---|---|---|---|---|---|---|---|
| the-racket-1927 | The Racket | movie | 1927 | no | 84 | NA | NA |
| Meryl Streep | Meryl Streep | actor |
There are a couple of very important things about this structure that make it work smoothly (and in some cases, at all) in Gephi.
- id is a column header that Gephi looks for in both the node and edge list. It should be a unique identifer for each person and movie that will appear on the graph. If this id isn't written then same as the
sourceortargetin the edges list, this won't work. - label is another term that Gephi looks for. This will be the label that you can toggle on and off on your graph.
- type isn't a term that Gephi looks for, but it's been added here so that we can easily distinguish between people and movies in the graph.
| source | target |
|---|---|
| the-shape-of-water-2017 | Octavia Spencer |
| a-streetcar-named-desire-1951 | Marlon Brando |
source and target are both terms that Gephi looks for in an edge list.
Download the network data and unzip it wherever you'd like.
Open Gephi and click on 'New Project'
Click on the 'Data Laboratory' tab at the top of the interface.
Click the ‘Import Spreadsheet’ tab.
Select and import the nodes data - leave all of the defaults except you'll want to select append to existing workspace instead of create new workspace.
Repeat this step with the 'edges' table
When the edges and nodes data are imported, they will be displayed in the ‘Data Laboratory’ window.
Click on the ‘Overview’ tab at the top of the screen to access functions you will use in the remainder of the exercise.
You will see the imported data rendered as a network visualization in the ‘Graph’ window. As network data grows in complexity the default visualization becomes fairly useless for exploring the network.
In order to better navigate the data it is possible to utilize a layout from the ‘Layout’ tab, on the left hand side of the screen, to generate a new visualization. Try experimenting with a couple of different layouts until you feel it is easier to interpret the network (e.x. clusters are distinct, nodes do not overlap). Note that each layout has additional features that can be leveraged in the Layout section of the interface.
We want to distinguish between the people and the movies in our data. To recolor according to these categories, select the ‘Partition’ tab in the top left corner of the Overview panel, and then the ‘Nodes’ tab located just below that. If a drop down menu does not appear, you may need to click the refresh (green arrows) button.
The dropdown should consist of each column heading in our nodes csv. Select ‘type’ and hit the ‘Apply’ button. You should see the nodes re-colored according to category. You will probably also want to turn the node labels on. The node label toggle switch is the dark-colored ‘T’ near the bottom left corner of the Graph section. You can adjust the text size either by clicking on the font button along the bottom or by moving the rightmost slider left and right to increase and decrease the size. Label color can be adjusted to the right of the slider.
On the right side of the interface, under the ‘Statistics’ tab, you’ll find a Network Overview menu with a number of metrics listed. These metrics are helpful for analyzing the graph, and for visualizing the graph in different ways.
Click ‘Run’ next to a metric to generate a data report window. The measurement for the entire graph will appear in the Network Overview, and the measurement for each node will be also be added to the Data Laboratory.
- Average Degree - The average number of edges connected to a node
- Avg. Weighted Degree - The average sum of the weights of edges connected to a node
- Network Diameter - The longest shortest path between nodes within the graph
- Graph Density - Measures how close the graph is to complete
- HITS - Computes two values for each node; How valuable information stored at that node is & the quality of the nodes links
- Modularity - Community detection algorithm
- PageRank - Ranks nodes “pages” according to how often a user following links will non-randomly reach the node “page”
- Connected Components - Determines the number of connected components in the network
- Avg. Clustering Coefficient - Averages how nodes are embedded in their neighborhood
- Eigenvector Centrality - A measure of node importance in a network based on a node’s connections; the sum of the centrality measures of all nodes connected to a node
- Avg. Path Length - The average number of steps to get from one randomly selected node to another
Once you’ve generated metrics, you can apply them to the graph visualization. Return to the left sidebar and click on the ‘Ranking’ tab, and then click the ‘Nodes’ tab just below it. Notice the icons to the right of the ‘Nodes’ and ‘Edges’ tab -- they allow you to resize and recolor both the nodes and the node labels. We’ll want to select the series of circles - the node size option.
Select 'Ranking' and then ‘Eigenvector Centrality’ from the drop down menu and click ‘Apply.' You may need to adjust the maximum size to better show the distribution and differences in the metric. If you stopped the ‘Layout’ algorithm, you may want to run it again with the ‘Prevent Overlap’ option selected, as resizing may have created more overlaps.
Try resizing nodes based on different metrics to get a feel for the kinds of visualizations you can create based on these statistics.
Before sharing your visualization, you may want to change the appearance of your graph. The ‘Preview’ window includes a number of options to improve the aesthetics of the graph. Click the ‘Refresh’ button to bring your graph into the window, and use the controls along the left side to make changes. The presets are good places to start, but you may need to tweak things from there. Changes will not be made automatically -- you must use the ‘Refresh’ button. This is a very large canvas, so you may lose your graph. The 'Reset zoom' button at the bottom of your screen is your friend here.
To manually reposition nodes, you will need to return to the ‘Overview’ window, make sure that the Layout algorithm is turned off, select the ‘Dragging’ tool (the hand icon along the left side of the graph window), and click and drag a node to reposition it.
Take a moment to change some of the aesthetics of your graph.
You can export an image (SVG, PDF, or PNG) of the entire graph using the button in the bottom left corner, and use an editor such as Inkscape or Illustrator to add a title, add a legend, or crop the image. You can also save the all workspaces as a .gephi file, and you will be able to return to the workspaces as you left them. You can also export a single graph file by selecting File->Export->Graph File and saving the file as.gefx, although some aesthetic changes will be lost. More information about file support is available on the Gephi website.