Efficient hypothesis testing for community detection in heterogeneous networks

TL;DR

This paper introduces an efficient hypothesis testing method for community detection in heterogeneous networks, leveraging graph dissimilarity measures to improve accuracy without prior knowledge of community count.

Contribution

The authors propose a novel hypothesis test based on graph dissimilarities and a two-stage bipartitioning algorithm for simultaneous community detection and structure identification.

Findings

Outperforms existing community detection methods on synthetic networks.

Effectively handles heterogeneity in node degrees.

Accurately determines the number of communities in real networks.

Abstract

Identifying communities in networks is a fundamental and challenging problem of practical importance in many fields of science. Current methods either ignore the heterogeneous distribution of nodal degrees or assume prior knowledge of the number of communities. Here we propose an efficient hypothesis test for community detection based on quantifying dissimilarities between graphs. Given a random graph, the null hypothesis is that it is of degree-corrected Erd\"{o}s-R\'{e}nyi type. We compare the dissimilarity between them by a measure incorporating the vertex distance distribution, the clustering coefficient distribution, and the alpha-centrality distribution, which is used for our hypothesis test. We design a two-stage bipartitioning algorithm to uncover the number of communities and the corresponding structure simultaneously. Experiments on synthetic and real networks show that our method outperforms state-of-the-art ones.