Line Graphs, Link Partitions and Overlapping Communities

TL;DR

This paper introduces a method for detecting overlapping communities in networks by partitioning links through the line graph, enabling nodes to belong to multiple communities and addressing degree heterogeneity.

Contribution

It presents a novel approach using line graph partitions to identify overlapping communities, including a weighted version to handle degree heterogeneity.

Findings

Link partitioning reveals overlapping communities effectively.

Weighted line graphs improve community detection in heterogeneous networks.

Any node partition algorithm can be adapted for link community detection.

Abstract

In this paper, we use a partition of the links of a network in order to uncover its community structure. This approach allows for communities to overlap at nodes, so that nodes may be in more than one community. We do this by making a node partition of the line graph of the original network. In this way we show that any algorithm which produces a partition of nodes can be used to produce a partition of links. We discuss the role of the degree heterogeneity and propose a weighted version of the line graph in order to account for this.