|Dynamic Community Finding|
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
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.
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]
C++ implementation of the dynamic community tracking method is provided
for non-commercial use. Documentation and sample files are provided in
Download: Linux 64-bit binary [Version 20101020]
Download: Mac OSX 10.6 64-bit
binary [Version 20101020]