Robustness of community structure in networks

TL;DR

This paper introduces a method to assess the statistical significance of community structures in networks by measuring their robustness to small perturbations, helping distinguish meaningful communities from random artifacts.

Contribution

It proposes a novel approach to quantify community significance through network perturbation and robustness measurement, applicable to real and synthetic networks.

Findings

Community structure robustness correlates with significance.

The method effectively distinguishes meaningful communities from random noise.

Applicable to both real-world and simulated networks.

Abstract

The discovery of community structure is a common challenge in the analysis of network data. Many methods have been proposed for finding community structure, but few have been proposed for determining whether the structure found is statistically significant or whether, conversely, it could have arisen purely as a result of chance. In this paper we show that the significance of community structure can be effectively quantified by measuring its robustness to small perturbations in network structure. We propose a suitable method for perturbing networks and a measure of the resulting change in community structure and use them to assess the significance of community structure in a variety of networks, both real and computer generated.