Alternative approach to community detection in networks

TL;DR

This paper introduces a new community detection method based on local definitions that overcomes the resolution limit of traditional modularity optimization, providing more accurate community identification.

Contribution

It proposes a novel approach using merit factors based on Radicchi et al's community definitions, avoiding the resolution limit problem.

Findings

New merit factors based on local community definitions

Avoidance of the resolution limit in community detection

More accurate detection of small communities

Abstract

The problem of community detection is relevant in many disciplines of science and modularity optimization is the widely accepted method for this purpose. It has recently been shown that this approach presents a resolution limit by which it is not possible to detect communities with sizes smaller than a threshold which depends on the network size. Moreover, it might happen that the communities resulting from such an approach do not satisfy the usual qualitative definition of commune, i.e., nodes in a commune are more connected among themselves than to nodes outside the commune. In this article we introduce a new method for community detection in complex networks. We define new merit factors based on the weak and strong community definitions formulated by Radicchi et al (Proc. Nat. Acad. Sci. USA 101, 2658-2663 (2004)) and we show that this local definitions avoid the resolution limit problem found in the modularity optimization approach.