A new measure of modularity density for community detection

TL;DR

This paper introduces a new modularity density metric for community detection in networks, which outperforms existing metrics in accuracy, bias reduction, and computational efficiency, especially in heterogeneous networks.

Contribution

A novel modularity density measure is proposed, demonstrating improved community detection performance and theoretical advantages over previous versions.

Findings

Superior detection of weakly-separated communities

Bias-free maximization of the new metric

Comparable or faster computational performance

Abstract

Using an intuitive concept of what constitutes a meaningful community, a novel metric is formulated for detecting non-overlapping communities in undirected, weighted heterogeneous networks. This metric, modularity density, is shown to be superior to the versions of modularity density in present literature. Compared to the previous versions of modularity density, maximization of our metric is proven to be free from bias and better detect weakly-separated communities particularly in heterogeneous networks. In addition to these characteristics, the computational running time of our modularity density is found to be on par or faster than that of the previous variants. Our findings further reveal that community detection by maximization of our metric is mathematically related to partitioning a network by minimization of the normalized cut criterion.