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Though much work has been done on ensemble clustering in data mining, theapplication of ensemble methods to community detection in networks is in itsinfancy. In this paper, we propose two ensemble methods: ENDISCO and MEDOC.ENDISCO performs disjoint community detection. In contrast, MEDOC performsdisjoint, overlapping, and fuzzy community detection and represents the firstever ensemble method for fuzzy and overlapping community detection. We runextensive experiments with both algorithms against both synthetic and severalreal-world datasets for which community structures are known. We show thatENDISCO and MEDOC both beat the best-known existing standalone communitydetection algorithms (though we emphasize that they leverage them). In the caseof disjoint community detection, we show that both ENDISCO and MEDOC beat anexisting ensemble community detection algorithm both in terms of multipleaccuracy measures and run-time. We further show that our ensemble algorithmscan help explore core-periphery structure of network communities, identifystable communities in dynamic networks and help solve the "degeneracy ofsolutions" problem, generating robust results.

Ensemble-Based Discovery of Disjoint, Overlapping and Fuzzy Community Structures in Networks