Open Access
August 2012 Consistency of community detection in networks under degree-corrected stochastic block models
Yunpeng Zhao, Elizaveta Levina, Ji Zhu
Ann. Statist. 40(4): 2266-2292 (August 2012). DOI: 10.1214/12-AOS1036


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.


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Yunpeng Zhao. Elizaveta Levina. Ji Zhu. "Consistency of community detection in networks under degree-corrected stochastic block models." Ann. Statist. 40 (4) 2266 - 2292, August 2012.


Published: August 2012
First available in Project Euclid: 23 January 2013

zbMATH: 1257.62095
MathSciNet: MR3059083
Digital Object Identifier: 10.1214/12-AOS1036

Primary: 62G20

Keywords: Community detection , consistency , degree-corrected stochastic block models

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.40 • No. 4 • August 2012
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