FOCS: Fast Overlapped Community Search

Abstract

Discovery of natural groups of similarly functioning individuals is a key task in analysis of real world networks. Also, overlap between community pairs is commonplace in large social and biological graphs, in particular. In fact, overlaps between communities are known to be denser than the non-overlapped regions of the communities. However, most of the existing algorithms that detect overlapping communities assume that the communities are denser than their surrounding regions, and falsely identify overlaps as communities. Further, many of these algorithms are computationally demanding and thus, do not scale reasonably with varying network sizes. In this article, we propose Fast Overlapped Community Search (FOCS), an algorithm that accounts for local connectedness in order to identify overlapped communities. FOCS is shown to be linear in number of edges and nodes. It additionally gains in speed via simultaneous selection of multiple near-best communities rather than merely the best, at each iteration. FOCS outperforms some popular overlapped community finding algorithms in terms of computational time while not compromising with quality.