Validation of community robustness

TL;DR

This paper introduces a statistical validation method for community detection in networks, assessing whether detected communities are significant or due to chance by analyzing stability under graph perturbations.

Contribution

The paper presents a novel methodology combining perturbation strategies and null models to statistically validate community structures in networks.

Findings

Method effectively distinguishes significant communities from random noise.

Applied to both simulated and real datasets, confirming robustness of community detection.

Provides a framework for assessing community detection reliability.

Abstract

The large amount of work on community detection and its applications leaves unaddressed one important question: the statistical validation of the results. In this paper we present a methodology able to clearly detect if the community structure found by some algorithms is statistically significant or is a result of chance, merely due to edge positions in the network. Given a community detection method and a network of interest, our proposal examines the stability of the partition recovered against random perturbations of the original graph structure. To address this issue, we specify a perturbation strategy and a null model to build a set of procedures based on a special measure of clustering distance, namely Variation of Information, using tools set up for functional data analysis. The procedures determine whether the obtained clustering departs significantly from the null model. This strongly supports the robustness against perturbation of the algorithm used to identify the community structure. We show the results obtained with the proposed technique on simulated and real datasets.