Stochastic blockmodels and community structure in networks

TL;DR

This paper improves stochastic blockmodels by incorporating degree variation, leading to better community detection in real-world networks with broad degree distributions.

Contribution

It introduces a degree-corrected stochastic blockmodel and a heuristic algorithm that significantly enhances community detection accuracy.

Findings

Degree-corrected models outperform uncorrected ones in synthetic networks.

The proposed heuristic effectively detects communities in real-world networks.

Incorporating degree variation improves the model's fit to real data.

Abstract

Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broad degree distributions that can significantly distort the results. Here we demonstrate how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks. We also propose a heuristic algorithm for community detection using this objective function or its non-degree-corrected counterpart and show that the degree-corrected version dramatically outperforms the uncorrected one in both real-world and synthetic networks.