This page contains supplementary material for the paper:

D. Greene, D. Doyle, and P. Cunningham. (2010), "Tracking Dynamic Communities in Large Social Networks". University College Dublin Technical Report UCD-CSI-2011-06, May 201

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Description

Real-world social networks from a variety of domains can naturally be mod- eled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Therefore, researchers have be- gun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking communities which persist over time in dynamic networks, where each community is characterized by a series of significant evolutionary events. This model is used to motivate a scalable community-tracking strategy for efficiently identifying dynamic communities.

Datasets

We provide here 3 sets of 4 types of dynamic benchmark graphs, containing embedded disjoint and overlapping communities.

Download benchmark data (86 MB) [October 2010]

These datasets were created using the following dynamic network generator. This tool is based on the static network generation tool written by Andrea Lancichinetti & Santo Fortunato. The source for the dynamic tool is made available under the GPL:

Download dynamic benchmark generator - source (340k) [Version 20101020]

Software

A C++ implementation of the dynamic community tracking method is provided for non-commercial use. Documentation and sample files are provided in the archive.

Download: Linux 64-bit binary [Version 20101020]

Download: Mac OSX 10.6 64-bit binary [Version 20101020]