Stochastic blockmodels and community structure in networks

Details Stochastic blockmodels and community structure in networks Stochastic blockmodels and community structure in networks

2010-08-23

arxiv.org/abs/1008.3926v1

Phys. Rev. E 83, 016107 (2011)

Physics

Physics and Society

11 pages, 3 figures

Scientific paper

10.1103/PhysRevE.83.016107

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.

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