Dynamic Community Finding
This page contains supplementary material for the paper:
D. Greene, D. Doyle, and P. Cunningham. (2010), "Tracking the evolution of communities in dynamic social networks". Proc. International Conference on Advances in Social Networks Analysis and Mining (ASONAM’10) (Second Best Paper Award)
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Real-world social networks from a variety of domains can naturally be modeled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Therefore, researchers have begun 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.
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.
Dynamic network generator [November 2015]
A C++ implementation of the dynamic community tracking method is available as a repository on GitHub, which includes documentation and sample files.
Older binary versions are available, but no longer supported.
Download: Linux 64-bit binary [Version 20101020]
Download: Mac OSX 10.6 64-bit
binary [Version 20101020]
For further information please contact Derek Greene.