Overlapping community detection in networks based on link partitioning and partitioning around medoids
This addresses the problem of identifying overlapping communities in networks for researchers and practitioners, but it is incremental as it builds on existing partitioning techniques.
The paper tackles overlapping community detection in networks by proposing LPAM, a method that uses link partitioning and partitioning around medoids with distance functions like commute distance, and reports exact solutions for small to medium networks and heuristic solutions for large ones.
In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.