Ensemble Detection and Analysis of Communities in Complex Networks

Abstract

Though much work has been done on ensemble clustering in data mining, the application of ensemble methods to community detection in networks is in its infancy. In this article, we propose MeDOF, an ensemble method which performs disjoint, overlapping, and fuzzy community detection and represents one of the first ever ensemble methods for fuzzy and overlapping community detection. We run extensive experiments on both synthetic and several real-world datasets for which community structures are known. We show that MeDOF beats the best-known existing stand-alone community detection algorithms. We further show that MeDOF can help explore core-periphery structure of network communities, identify stable communities in dynamic networks, and help solve the “degeneracy of solutions” problem, generating robust results.