2 research outputs found
Ensemble-based Overlapping Community Detection using Disjoint Community Structures
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].Comment: 31 pages, 7 tables, 3 figures, Knowledge-Based System
Ensemble-Based Discovery of Disjoint, Overlapping and Fuzzy Community Structures in Networks
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