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Tracking communities in Dynamic Social Graphs with algorithms such as Timerank, GED and more. This is a fork from Ilias Sarantopoulos' original implementations.

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Tracking Communities in Dynamic Graphs with Applications to Social Networks

This repository is a fork of the code developed around the MSc Thesis:

Ilias Sarantopoulos, “Tracking the Evolution of Communities in Dynamic Social Networks” MSc Thesis, Athens University of Economics and Business, 2017

and updated to work with Python 3 in a Windows environment. More experiments and optimizations are going to be done.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

All packages available through pip can be found in the file 'requirements.txt'. You can install them by running:

pip install -r requirements.txt

There are 3 more external packages used: 'GED' is the python repository available in demokritos-github for Group Evolution Discovery. 'omni' is the C++ package for NMI evaluation developed by and available in onmi-Github-repo and 'ncp' is the package available in NNTF-Github-repo for Non Negative Tensor Factorization with the least alternate squares method.

Running Experiments

Each of the scripts in the root directory that starts with "experiments_{dataset}" describes a full set of comparative experiments for a specific dataset. The user should change the path to the files where the dataset exists. Running the script will produce a txt file "results_{dataset}" which will contain the evaluated results of each method (GED, NNTF, Muturank).

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Tracking communities in Dynamic Social Graphs with algorithms such as Timerank, GED and more. This is a fork from Ilias Sarantopoulos' original implementations.

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