A Method to Find Community Structures Based on Information Centrality

TL;DR

This paper introduces a hierarchical clustering algorithm that detects community structures in networks by iteratively removing edges with the highest information centrality, proving effective especially in complex, mixed communities.

Contribution

It presents a novel method based on information centrality for community detection, extending and improving upon Girvan and Newman's approach.

Findings

Effective in detecting complex community structures

Performs well on both synthetic and real-world networks

Operates with a computational complexity of O(n^4)

Abstract

Community structures are an important feature of many social, biological and technological networks. Here we study a variation on the method for detecting such communities proposed by Girvan and Newman and based on the idea of using centrality measures to define the community boundaries (M. Girvan and M. E. J. Newman, Community structure in social and biological networks Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002)). We develop an algorithm of hierarchical clustering that consists in finding and removing iteratively the edge with the highest information centrality. We test the algorithm on computer generated and real-world networks whose community structure is already known or has been studied by means of other methods. We show that our algorithm, although it runs to completion in a time O(n^4), is very effective especially when the communities are very mixed and hardly detectable by the other methods.