Limited resolution and multiresolution methods in complex network community detection

TL;DR

This paper analyzes the resolution limits of community detection methods in complex networks, focusing on two multiresolution approaches, and evaluates their effectiveness through analytical and experimental methods.

Contribution

It provides an analytical comparison of two multiresolution community detection methods and assesses their performance on test networks using simulated annealing.

Findings

Resolution limits restrict detection of small communities

Multiresolution methods can reveal communities at different scales

Analytical and experimental evaluation demonstrates effectiveness

Abstract

Detecting community structure in real-world networks is a challenging problem. Recently, it has been shown that the resolution of methods based on optimizing a modularity measure or a corresponding energy is limited; communities with sizes below some threshold remain unresolved. One possibility to go around this problem is to vary the threshold by using a tuning parameter, and investigate the community structure at variable resolutions. Here, we analyze the resolution limit and multiresolution behavior for two different methods: a q-state Potts method proposed by Reichard and Bornholdt, and a recent multiresolution method by Arenas, Fernandez, and Gomez. These methods are studied analytically, and applied to three test networks using simulated annealing.