  While there has been a plethora of approaches for detecting disjoint
communities from real-world complex networks, some methods for detecting
overlapping community structures have also been recently proposed. In this
work, we argue that, instead of developing separate approaches for detecting
overlapping communities, a promising alternative is to infer the overlapping
communities from multiple disjoint community structures. We propose an
ensemble-based approach, called EnCoD, that leverages the solutions produced by
various disjoint community detection algorithms to discover the overlapping
community structure. Specifically, EnCoD generates a feature vector for each
vertex from the results of the base algorithms and learns which features lead
to detect densely connected overlapping regions in an unsupervised way. It
keeps on iterating until the likelihood of each vertex belonging to its own
community maximizes. Experiments on both synthetic and several real-world
networks (with known ground-truth community structures) reveal that EnCoD
significantly outperforms nine state-of-the-art overlapping community detection
algorithms. Finally, we show that EnCoD is generic enough to be applied to
networks where the vertices are associated with explicit semantic features. To
the best of our knowledge, EnCoD is the second ensemble-based overlapping
community detection approach after MEDOC [1].
