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". In Proc. International Conference on Advances in Social Networks Analysis and Mining (ASONAM'10). [PDF] [BibTeX]
Description
Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. We have proposed a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model has been used to motivate a new community-matching strategy for efficiently identifying and tracking dynamic communities.
Downloads
Data: Dynamic benchmark graphs containing embedded communities
Software: A C++ implementation of the dynamic community finding method is provided for non-commercial use. Documentation and sample files are provided in the archive.
Download: Linux 64-bit binary [Version 20101020]
Download: OSX 10.6 64-bit binary [Version 20101020]
If you use this data or software in your research, we encourage you to cite the associated paper provided above.
Contact
For further information regarding the software and data, please contact Derek Greene.
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