Consistency of community detection in networks under degree-corrected stochastic block models

TL;DR

This paper develops a theoretical framework for community detection in networks using degree-corrected stochastic block models, showing when different methods are consistent and how degree variability affects their performance.

Contribution

It establishes general theory for community detection consistency under degree-corrected models and compares various criteria, highlighting when degree correction improves results.

Findings

Degree-corrected methods are consistent under broader conditions.

Likelihood-based methods do not require parameter constraints for consistency.

Degree correction is beneficial mainly when node degrees within communities are highly variable.

Abstract

Community detection is a fundamental problem in network analysis, with applications in many diverse areas. The stochastic block model is a common tool for model-based community detection, and asymptotic tools for checking consistency of community detection under the block model have been recently developed. However, the block model is limited by its assumption that all nodes within a community are stochastically equivalent, and provides a poor fit to networks with hubs or highly varying node degrees within communities, which are common in practice. The degree-corrected stochastic block model was proposed to address this shortcoming and allows variation in node degrees within a community while preserving the overall block community structure. In this paper we establish general theory for checking consistency of community detection under the degree-corrected stochastic block model and compare several community detection criteria under both the standard and the degree-corrected models. We show which criteria are consistent under which models and constraints, as well as compare their relative performance in practice. We find that methods based on the degree-corrected block model, which includes the standard block model as a special case, are consistent under a wider class of models and that modularity-type methods require parameter constraints for consistency, whereas likelihood-based methods do not. On the other hand, in practice, the degree correction involves estimating many more parameters, and empirically we find it is only worth doing if the node degrees within communities are indeed highly variable. We illustrate the methods on simulated networks and on a network of political blogs.