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

## TL;DR

This paper introduces LPAM, a novel method for detecting overlapping communities in networks by partitioning the line graph using link-based clustering and medoids, evaluated on real and synthetic networks.

## Contribution

The paper proposes a new link partitioning approach using partitioning around medoids for overlapping community detection, including a heuristic for large networks.

## Key findings

- Exact solutions for small and medium networks
- Heuristic solutions for large networks
- Effective performance on real and synthetic data

## Abstract

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.