Community Detection through Likelihood Optimization: In Search of a Sound Model

TL;DR

This paper compares different statistical null models for community detection in networks, introducing a new model that better fits real-world data and analyzing algorithms for likelihood optimization.

Contribution

It provides a comprehensive theoretical and empirical comparison of community detection models, introducing a new null model with superior statistical properties.

Findings

The new null model outperforms existing models in likelihood on real networks.

Likelihood optimization algorithms are effective for community detection.

The study offers insights into model selection for network analysis.

Abstract

Community detection is one of the most important problems in network analysis. Among many algorithms proposed for this task, methods based on statistical inference are of particular interest: they are mathematically sound and were shown to provide partitions of good quality. Statistical inference methods are based on fitting some random graph model (a.k.a. null model) to the observed network by maximizing the likelihood. The choice of this model is extremely important and is the main focus of the current study. We provide an extensive theoretical and empirical analysis to compare several models: the widely used planted partition model, recently proposed degree-corrected modification of this model, and a new null model having some desirable statistical properties. We also develop and compare two likelihood optimization algorithms suitable for the models under consideration. An extensive empirical analysis on a variety of datasets shows, in particular, that the new model is the best one for describing most of the considered real-world complex networks according to the likelihood of observed graph structures.