Compacting Large and Loose Communities
Venue
Asian Conference on Pattern Recognition (2013) (to appear)
Publication Year
2013
Authors
Chandrashekhar V., Shailesh Kumar, C. V. Jawahar
BibTeX
Abstract
Detecting compact overlapping communities in large networks is an important pattern
recognition problem with applications in many domains. Most community detection
algorithms trade-off between community sizes, their compactness and the scalability
of finding communities. Clique Percolation Method (CPM) and Local Fitness
Maximization (LFM) are two prominent and commonly used overlapping community
detection methods that scale with large networks. However, significant number of
communities found by them are large, noisy, and loose. In this paper, we propose a
general algorithm that takes such large and loose communities generated by any
method and refines them into compact communities in a systematic fashion. We define
a new measure of community-ness based on eigenvector centrality, identify loose
communities using this measure and propose an algorithm for partitioning such loose
communities into compact communities. We refine the communities found by CPM and
LFM using our method and show their effectiveness compared to the original
communities in a recommendation engine task.
