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Tracking communities accross time:

It tracks network communities across consecutive time frames. In particular it detects the evolutionary phenomenon a community can sustain from one time frame to another:

  • continuing
  • shrinking
  • growing
  • split
  • merge
  • dissolving
  • No_event (when none of the above events holds) The code is based on the implementation of [1]

File Structure & running

  • It contains 6 .py (thon) files , ,, , ,
  • To run from terminal: python .json
  • output: a file named 'results.csv', the format is: window ID, community ID, window ID, community ID, event
  • In there are 2 parameters Alpha,Beta. These are the thresholds for the event identification. Should be between [0.1,1]

Input data format

The file given as input to the GED algorithm must adhere to the following JSON template:

     [      //array of windows (i.e. timeframes)
             [     //array of communities in each window
                [     //array of edges in each community
                    [   //array containing two node ids between which an edge exists (this is an edge of the community)

We assume that the data set has been split into time frames and that communities have been discovered in each time frame.

An example input file is provided in the "input/example" directory to test GED, consisting of 20 windows. input/example/community_edges.json

                                             <-------  community ---------->   <-------  community ---------->
time-step:1 --> {"windows":[{"communities":[ [[id1,id2],[id2,id3],[id3,id1]], [[id4,id5],[id5,id6],[id6,id7]] ]
time-step:2 --> {"windows":[{"communities":[ [[id8,id9],[id9,id10],[id10,id1]], [[id11, id12],[id12,id13],[id13,id11]] ]


  • The output file generated by GED has the following format: ,,,,
  • E.g. 0,40,1,42,continuing means that community 40 in window 0 has evolved (corresponds) to community 42 in the next window (window 1) and the associated event with this evolution is continue.
  • Windows are numbered consecutively starting from 0. The same holds for the communities in each window.


[1]: Piotr Brodka, Stanislaw Saganowski, and Przemyslaw Kazienko (2011), Tracking group evolution in social networks, International Conference on Social Informatics, 316--319, Springer


community-tracking - NCSR Demokritos module Copyright 2016 Michail Apostolou

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at:


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