Title:
Detecting Communities from Given Seeds in Social Networks
Detecting Communities from Given Seeds in Social Networks
Author(s)
Riedy, Jason
Bader, David A.
Jiang, Karl
Pande, Pushkar
Sharma, Richa
Bader, David A.
Jiang, Karl
Pande, Pushkar
Sharma, Richa
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
Analyzing massive social networks challenges both high-performance computers and human under-
standing. These massive networks cannot be visualized easily, and their scale makes applying complex
analysis methods computationally expensive. We present a region-growing method for finding a smaller,
more tractable subgraph, a community, given a few example seed vertices. Unlike existing work, we focus
on a small number of seed vertices, from two to a few dozen. We also present the first comparison between five algorithms for expanding a small seed set into a community. Our comparison applies these algorithms
to an R-MAT generated graph component with 240 thousand vertices and 32 million edges and evaluates
the community size, modularity, Kullback-Leibler divergence, conductance, and clustering coefficient. We find that our new algorithm with a local modularity maximizing heuristic based on Clauset, Newman,
and Moore performs very well when the output is limited to 100 or 1000 vertices. When run without a
vertex size limit, a heuristic from McCloskey and Bader generates communities containing around 60% of
the graph's vertices and having a small conductance and modularity appropriate to the result size. A
personalized PageRank algorithm based on Andersen, Lang, and Chung also performs well with respect
to our metrics.
Sponsor
Date Issued
2011-02-22
Extent
Resource Type
Text
Resource Subtype
Technical Report