Abstract: 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.
|Dynamic Community Finding : Benchmark data||1 zip-compressed file archive
|Dynamic Community Tracking Tool (Software) : Linux 64-bit binary||1 gzip-compressed file archive
|Dynamic Community Tracking Tool (Software) : Mac OSX 10.6 64-bit binary||1 gzip-compressed file archive