About

FETA stands for "Framework for Evolving Topology Analysis", and is a multi-purpose java-based software for the analysis of evolving networks and models for their topology, based on FETA2 by Richard Clegg.

What can I use FETA for?

• Obtaining time series of measurements on growing directed and undirected networks
• Growing networks from a range of models and combinations of these
• Fitting models to timestamped network data.

What can't I use FETA for?

• Networks with disappearing nodes or edges
• Weighted networks/multigraphs

Changes from FETA2

• Flexibility to specify models which may change over time
• More graph file types supported
• Scripts for configuring now written in JSON rather than XML
• Different types of network growth considered

Contact

If you have questions, comments, ideas or are having bugs, please don't hesitate to get in touch. Drop me an email at n.a.arnold@qmul.ac.uk

Citation

Please cite as:

Arnold, Naomi A., Raul J. Mondragón, and Richard G. Clegg. "Likelihood-based approach to discriminate mixtures of network models that vary in time." Scientific reports 11.1 (2021): 1-13.

Building and running

Requirements

This software runs using Java, so if you haven't already, install the latest version here.

The jar file is built using Apache Ant, which can be found here.

Optionally, I have some scripts for gnuplot in the tutorial for plotting - you can find gnuplot here

Installation

First clone the repository, either manually or using the command:

git clone https://github.com/narnolddd/FETA3.git

For everything else, jump into the FETA3 folder if you haven't already:

cd FETA3

Building

Then run the command:

ant jar

to make the jar file.

Running

Once you have the jar file, FETA is executed from the command line as:

java -jar feta3-1.0.0.jar scripts/[some-script-name].json

The single argument scripts/[some-script-name].json is a JSON config file telling FETA what action to perform. The next section describes how this works.

Examples

Obtaining time series of network quantities

Let's do an example of obtaining a time series of measurements from a timestamped network dataset. In the data folder there's a file 'cit-HepPh-new.txt' which is a timestamped citation network dataset from ArXiV high energy physics phenomenology, available from SNAP. We will use the script MeasureCitations.json in the tutorial folder, which looks like:

{
  "Data": {
    "GraphInputFile": "data/cit-HepPh-new.txt",
    "GraphInputType": "NNT",
    "Directed": false
  },
  "Action": {
    "Measure": {
      "Start": 706492800,
      "Interval":604800,
      "MaxNodes": 34343,
      "Statistics": ["NoNodes", "NoLinks", "AverageDegree", "MaxDegree","Singletons","MeanSquaredDegree", "Assortativity", "Clustering", "TriangleCount"],
      "PrintHeader": true,
      "FileName":"tutorial/CitationsTS.dat"
    }
  }
}

The Data tag tells FETA what type of input to expect:

• The GraphInputFile tag specifies the location of the network file to read. Acceptable formats are tab/comma separated edgelists, with or without timestamps.
• GraphInputType allows either "NNT" or"NN" - this refers to whether or not the edgelist is timestamped. "NNT" is for edges which look like NODE-1 NODE-2 TIMESTAMP, "NN" for NODE-1 NODE-2. In the latter case, edges will be treated as arriving sequentially.
• Directed refers to whether the edges should be treated as directed or not - default false.

The Action tag tells FETA what to do with the inputted file; in this case we want to take measurements of it. We can specify:

• Start - time at which FETA will start taking measurements. This takes integers/longs. In this case, the timestamp type is a UNIX timestamp.

• Interval - time between measurements. In this case, we specify weekly measurements

• Stopping condition. This can either be a latest time (given as long) MaxTime, maximum number of nodes MaxNodes or links MaxLinks. This specifies how long the measurements should take place for - in this example the measurements will finish when the network size has reached 34343 nodes, the complete size of this network dataset.

• Statistics - a string vector of the different statistics desired. In this JSON I have listed all available statistics.

• FileName - where to write the measurements to. This will also generate one, or two files (depending on whether or not the network is directed) to store the degree distribution at different snapshots.

In the terminal, run the command

java -jar feta3-1.0.0.jar tutorial/MeasureCitations.json

It may take a minute or so to run, as the network file is quite big.

You should now have the file CitationsTS.dat in the tutorial folder. The columns are ordered as: timestamp, number of nodes, number of links, average degree, density (number of links/number of possible links), maximum degree, average clustering coeff, mean squared degree, degree assortativity.

To see what these time series look like, you can use your favourite plotting device. Mine happens to be pyplot. To use this you should have pandas instlaled for python3. I have written a script for plotting these measurements you've just calculated - run the command:

python3 tutorial/CitationsTS.py

and there should now be an .eps plot in your tutorial/plots folder which should look like this:

Whilst these plots are informative, note that we have treated the citation network edgelist as undirected, whereas since citations are always from one paper to another, it may more naturally be considered a directed network. What happens if we do the same process but treat the network as directed?

We're going to run the same command as before, but with the file tutorial/MeasureCitationsDirected.json which looks like:

{
  "Data": {
    "GraphInputFile": "data/cit-HepPh-new.txt",
    "GraphInputType": "NNT",
    "Directed": true
  },
  "Action": {
    "Measure": {
      "Start": 706492800,
      "Interval":604800,
      "MaxNodes": 34343,
      "Statistics": ["NoNodes", "NoLinks", "AverageDegree", "MaxDegree","Singletons","MeanSquaredDegree", "Assortativity", "Clustering", "TriangleCount"],
      "PrintHeader": true,
      "FileName":"tutorial/CitationsTSDirected.dat"
    }
  }
}

This is identical to the previous file tutorial/MeasureCitations.json apart from the Directed tag being changed from false to true, and a different filename given to print the measurements to.

Let's run the command

java -jar feta3-1.0.0.jar tutorial/MeasureCitationsDirected.json

Now, notice that some of the measurements which made sense for undirected networks may not make sense or at the very least need some adaptation for directed networks. For instance, 'average degree' will be split into two measurements of 'average in-degree' and 'average out-degree'. Also, the clustering coefficient is now ill-defined as the concept of a triangle of nodes in directed networks doesn't make much sense now. With this in mind, the columns are ordered as: timestamp, number of nodes, number of links, average in-degree, average out-degree (both identical), maximum in-degree, maximum out-degree, mean squared in-degree, mean squared out-degree, and four measures of degree assortativity (in-in, in-out, out-in, out-out).

Again, to generate some plots, run:

python3 tutorial/CitationsTSDirected.py

which will generate some .eps files in the plots folder. Compare with the plots generated when we treated the network as undirected.

Just for fun, we can plot the in/out/total degree distribution too. Run:

python3 tutorial/CitationsDegDist.py

giving the following plot:

Working with evolving network models

Whilst taking network measurements of timestamped is certainly one helpful feature of FETA, the main focus of the software is on a versatile modelling and model validation framework for evolving networks. For a mathematically detailed description of the framework, see the paper Likelihood-based assessment of dynamic networks by Richard Clegg, Ben Parker and Miguel Rio. For the purpose of using the code, I'll give a basic overview.

Network growth models are specified in two parts. The first is the Operation Model, which determines the type of graph transformation that occurs at each iteration. For example, adding a new node and connecting to 3 existing nodes, such as in the traditional Barabasi-Albert model. The second part is the Object Model. This model takes a network topology (possibly with some metadata) and assigns a probability to each of the nodes in the network to be chosen. For example, in the Barabasi-Albert model, the probability of a node being chosen is proportional to its degree.

Operation Model

For the operation model, we consider just two very simple types of growth operations which we'll call external and internal stars.

Either a new node joins the network and connects to a number of (existing or new) nodes as below, which we term an external star:

Alternatively, an existing node connects to a number of other existing nodes, which we call an internal star:

The full operation model specifies a sequence of these operations for the network evolution.

The current choices of Operation Model for the user are:

• Email - named as such to mimic email networks, with growth comprising a mixture of internal and external stars. User can specify NoRecipients which is the number of links for each star, and PropInternal which specifies the probability that the star stems from an internal node.
• PreferentialAttachment - named after the original Barabasi-Albert paper, with networks growing by addition of a single node connecting to a fixed number of existing nodes. User can specify InitDegree, the number of nodes each new node should connect to.

Object Model

The Object Model assigns, at each timestep, attachment probabilities to each of the nodes. The different models currently loaded in the software are:

• RandomAttachment - all nodes equally likely.
• DegreeModelComponent (Barabasi-Albert) - nodes of higher degree are more attractive
• DegreeWithAgeing (Dorogovtsev-Mendes) - like the above but older nodes become less attractive. Has parameter AgeingExponent controlling the effect of node's age on attractiveness.
• RankPreferentialAttachment (Fortunato, Flammini, Menczer) - with parameter Alpha. Positive values of alpha lead to oldest nodes being most attractive, vice versa for negative values.
• DegreePower Krapivsky, Redner, Leyvraz - nonlinear preferential attachment to node degree. Parameter Power species the degree power.
• TriangleClosure - aims to close triangles by nodes picking at random from the neighbourhood of the last l chosen nodes.
• TriangleClosureDegree - same as previous, but weighted towards higher degree nodes in this neighbourhood
• TriangleClosureInverseDegree - weighted towards lower degree nodes

In addition, the modelling framework allows for flexibility in two ways:

Model Mixtures

Firstly, mixtures of object models may be used, for example a model that is 'half BA and half random' to capture multiple factors driving attachment.

Time varying models

Secondly, models are allowed to vary throughout the time of a network's evolution, for example, growing for the first 1000 timesteps according to pure random attachment, from 1000-2000 as half-random half-BA, and 2000-3000 pure BA.

Growing a network from a specified model

One of the capabilities of FETA is to generate networks from the modelling framework described. Let's look at an example growing script called GrowExample1.json. For the sake of exhaustiveness, I've made it a bit more complicated than is probably necessary.

{
  "Data": {
    "GraphInputFile": "seed_graphs/loop-5.dat",
    "GraphOutputFile": "tutorial/GrowExample1.dat",
    "GraphInputType": "NNT",
    "GraphOutputType": "NNT",
    "Directed": false
  },
  "Action": {
    "Grow": {
      "Start": 1,
      "Interval": 10,
      "MaxTime": 3000
    }
  },
  "ObjectModel": [
    {
      "Start": 1,
      "End": 1000,
      "Components": [
        {
          "ComponentName": "feta.objectmodels.components.DegreeWithAgeing",
          "Weight": 1.0,
          "AgeingExponent": 0.8
        }
      ]
    },
    {
     "Start": 1000,
      "End": 2000,
      "Components": [
        {
          "ComponentName": "feta.objectmodels.components.RandomAttachment",
          "Weight": 0.5
        },
        {
          "ComponentName": "feta.objectmodels.components.DegreeModelComponent",
          "Weight": 0.5
        }
      ]
    },
    {
      "Start": 2000,
      "End": 3000,
      "Components": [
        {
          "ComponentName": "feta.objectmodels.components.RankPreferentialAttachment",
          "Weight": 1.0,
          "Alpha": 0.6
        }
      ]
    }
  ],
  "OperationModel":
  {
    "Name": "feta.operations.Email",
    "NoRecipients": 3,
    "PropInternal":0.2
  }
}

The tag GraphInputFile now points to the seed graph with which we want to start - here a file with edgelist comprising a loop of 5 nodes.

Run the command

java -jar feta3-1.0.0.jar tutorial/GrowExample1.json

Let's get some measurements on the network we've grown.

Run

java -jar feta3-1.0.0.jar tutorial/MeasureArtificial.json

and finally plot using:

python3 tutorial/ArtificialTS.py

(You should get something like the below in the tutorial/plots folder)

Subtleties

The object model must be fully specified for the whole time period that the network is growing. That is, the value for End tag on the last ObjectModel must be at least the value for MaxTime in Action. Similarly, the last value of each subsequent time interval must be equal to the start of the next (here 1-1000, 1000-2000, 2000-3000).

Model likelihood

The main feature of the FETA software, along with generating networks, is to calculate likelihoods of network object models for a timestamped network dataset. Please refer to the paper for an in depth description, but informally, the value c0 calculated is a rescaling of the log-likelihood ratio of the model proposed against pure random/uniform attachment. Then c0 > 1 should be interpreted as a model being more likely than the random attachment model, c0 < 1 less likely.

To do a quick example, run:

java -jar feta3-1.0.0.jar tutorial/CitationsBALikelihood.json

Let's look in closer detail at the JSON file:

{
  "Data": {
    "GraphInputFile": "data/cit-HepPh-new.txt",
    "GraphInputType": "NNT",
    "GraphOutputType": "NNT",
    "Directed": false
  },
  "Action": {
    "Likelihood": {
      "Start": 706492800,
      "Interval": 1,
      "MaxNodes": 10000,
      "OrderedData": false
    }
  },
  "ObjectModel": [
    {
      "Start": 706492800,
      "End": 1006492800,
      "Components": [
        {
          "ComponentName": "DegreeModelComponent",
          "Weight": 1.0
        }
      ]
    }
  ]
}

We are calculating the likelihood (c0) value of the degree preferential attachment (BA) model for the citation network as before, for the network's entire evolution from time 706492800 up to it reaching 10,000 nodes. The likelihood returned looks something like this:

c0 1.5773221701566862 raw 18497.567340787682 choices 40589

The value for raw is the log-likelihood of the model, whilst c0 is a value scaled by the number of node choices made (= exp(raw/choices)).

Model fitting

Another feature of FETA, following on from being able to calculate individual likelihoods, is to be able to fit a mixed model to a dataset, identifying the mixture of models which gives the highest likelihood for the dataset. Let's do an example for the citations network dataset - see the file tutorial/CitationsFitMixed.json:

{
  "Data": {
    "GraphInputFile": "data/cit-HepPh-new.txt",
    "GraphInputType": "NNT",
    "GraphOutputType": "NNT",
    "Directed": false
  },
  "Action": {
    "FitMixedModel": {
      "Start": 706492800,
      "Interval": 60400,
      "MaxNodes": 1000,
      "OrderedData": false,
      "Granularity": 100
    }
  },
  "ObjectModel": [
    {
      "Start": 706492800,
      "End": 1006492800,
      "Components": [
        {
          "ComponentName": "DegreeModelComponent"
        },
        {
          "ComponentName": "RandomAttachment"
        },
        {
          "ComponentName": "TriangleClosure",
          "Depth":1
        }
      ]
    }
  ]
}

Here we invoke the action FitMixedModel, a routine for finding the highest likelihood mixture of the model components chosen, here degree (BA), random attachment and triangle closure. Granularity refers to how fine-grained a search over all possible model mixtures (in terms of weights) - here granularity 100 means a search over the parameter space in increments of 0.01 for each model component weight.

Running the command:

java -jar feta3-1.0.0.jar tutorial/CitationsFitMixed.json

should yield a response something like this (not necessarily exactly the same as there is some randomness):

Max c0 : 1.3560584194081042 max like 130.36121188081304 choices 428
0.4 Degree
0.45 Random
0.15 TriangleClosure 1

This is the maximum likelihood mixture from the model components tested, but not necessarily a good model. How do we evaluate the performance of this model?

'Cloning' a network's operation model

Another useful feature of FETA, having defined the Operation Model earlier, is to be able to extract from a timestamped network the sequence of growth operations that occurred at each time step. The edgelist is processed iteratively starting at a given timestamp. Links arriving at the same timestamp as processed as follows:

• If the new links (viewed in isolation) form a k-star, that is, one node of degree k, k nodes of degree 1, then the operation comprising those links is processed as a star of k leaves.

• If the node at the centre of that star is a new node (not previously present in the edgelist), then that star is an external star.

• If the node at the centre is an existing node (already seen in the edgelist), then the star is an internal star.

• Anything more complex than this is processed as a series of links (or 1-stars), treated as arriving in the same order in which they appear in the edgelist.

Let's do an example with the citation network. Run

java -jar feta3-1.0.0.jar tutorial/CloneOperationModel.json

You'll find a file in the tutorial folder called CitationsOpModel.feta which contains the extracted Operation Model for the network up to 34343 nodes. An example line in the file is

706924800 STAR 9205238 LEAVES 203224 212068 212391 302149 302275 EXTERNAL 0

meaning, at time 706924800 the network grew by a new node (EXTERNAL) with id 9205238 joining connecting to 5 existing nodes, with ids given by the numbers following LEAVES, and 0 new nodes.

Having this, we may grow a network using this operation model, and whatever object model we like. Let's use the best-fitting model we found in the last section:

java -jar feta3-1.0.0.jar tutorial/GrowExample2.json

for a network grown in this way, but with object model as in the previous section, where we have put in the weights corresponding to those found having the highest likelihood:

{
  "Data": {
    "GraphInputFile": "data/cit-HepPh-new.txt",
    "GraphInputType": "NNT",
    "GraphOutputType": "NNT",
    "GraphOutputFile":"tutorial/GrowExample2.dat",
    "Directed": false
  },
  "Action": {
    "Grow": {
      "Start": 706492800,
      "Interval": 1000,
      "MaxNodes": 1000
    }
  },
  "ObjectModel": [
    {
      "Start": 706492800,
      "End": 1006492800,
      "Components": [
        {
          "ComponentName": "feta.objectmodels.components.DegreeModelComponent",
          "Weight": 0.46
        },
        {
          "ComponentName": "feta.objectmodels.components.RandomAttachment",
          "Weight":0.14
        },
        {
          "ComponentName": "feta.objectmodels.components.TriangleClosureDegree",
          "Weight":0.4
        }
      ]
    }
  ],
  "OperationModel":
  {
    "Name": "feta.operations.Clone",
    "FileName": "tutorial/CitationsOpModel.feta",
    "Start": 706492800
  }
}

As before, we can obtain measurements on quantities of interest using a file tutorial/MeasureCitationsArtificial.json and plot for comparison.