On Consistency of Community Detection in Networks

Details On Consistency of Community Detection in Networks On Consistency of Community Detection in Networks

2011-10-18

arxiv.org/abs/1110.3854v2

Mathematics

Statistics Theory

Scientific paper

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 block model was proposed to address this shortcoming, and allows variation in node degrees within a community while preserving the overall block model community structure. In this paper, we establish general theory for checking consistency of community detection under the degree-corrected block model, and compare several community detection criteria under both the standard and the degree-corrected block 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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