Detecting highly overlapping community structure by greedy clique expansion [article]

Conrad Lee, Fergal Reid, Aaron McDaid, Neil Hurley (Clique Research Cluster, University College Dublin, Ireland)
2010 arXiv   pre-print
In complex networks it is common for each node to belong to several communities, implying a highly overlapping community structure. Recent advances in benchmarking indicate that existing community assignment algorithms that are capable of detecting overlapping communities perform well only when the extent of community overlap is kept to modest levels. To overcome this limitation, we introduce a new community assignment algorithm called Greedy Clique Expansion (GCE). The algorithm identifies
more » ... inct cliques as seeds and expands these seeds by greedily optimizing a local fitness function. We perform extensive benchmarks on synthetic data to demonstrate that GCE's good performance is robust across diverse graph topologies. Significantly, GCE is the only algorithm to perform well on these synthetic graphs, in which every node belongs to multiple communities. Furthermore, when put to the task of identifying functional modules in protein interaction data, and college dorm assignments in Facebook friendship data, we find that GCE performs competitively.
arXiv:1002.1827v2 fatcat:l277qlm5o5dkxa5zss63kajdu4