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Overlapping community detection in networks based on link partitioning and partitioning around medoids

by   Alexander Ponomarenko, et al.
Higher School of Economics

In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters. The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph by means of link partitioning and partitioning around medoids. Partitioning around medoids is done through the use of a distance function defined on the set of nodes of the linear graph. In the present paper, we consider the commute distance and amplified commute distance functions as distance functions. The performance of the proposed method is demonstrated by computational experiments on real-life instances.


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