  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 paper, we propose two ensemble methods: ENDISCO and MEDOC.
ENDISCO performs disjoint community detection. In contrast, MEDOC performs
disjoint, overlapping, and fuzzy community detection and represents the first
ever ensemble method for fuzzy and overlapping community detection. We run
extensive experiments with both algorithms against both synthetic and several
real-world datasets for which community structures are known. We show that
ENDISCO and MEDOC both beat the best-known existing standalone community
detection algorithms (though we emphasize that they leverage them). In the case
of disjoint community detection, we show that both ENDISCO and MEDOC beat an
existing ensemble community detection algorithm both in terms of multiple
accuracy measures and run-time. We further show that our ensemble algorithms
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.
