Improving community detection via community association strength scores

TL;DR

This paper introduces community association strength scores as post-processing tools to enhance community detection, identify outliers, and detect nodes with multiple memberships in complex networks.

Contribution

It proposes three simple CAS scores that improve existing partitions and reveal overlapping or outlier nodes, addressing limitations of traditional community detection methods.

Findings

CAS scores improve node partition quality

They help detect outlier nodes not in any community

They assist in identifying nodes with multiple community memberships

Abstract

Community detection methods play a central role in understanding complex networks by revealing highly connected subsets of entities. However, most community detection algorithms generate partitions of the nodes, thus (i) forcing every node to be part of a community and (ii) ignoring the possibility that some nodes may be part of multiple communities. In our work, we investigate three simple community association strength (CAS) scores and their usefulness as post-processing tools given some partition of the nodes. We show that these measures can be used to improve node partitions, detect outlier nodes (not part of any community), and help find nodes with multiple community memberships.