Corrected Bayesian Information Criterion for Stochastic Block Models

Jianwei Hu, Hong Qin, Ting Yan and Yunpeng Zhao

Journal of the American Statistical Association, 2020, vol. 115, issue 532, 1771-1783

Abstract: Estimating the number of communities is one of the fundamental problems in community detection. We re-examine the Bayesian paradigm for stochastic block models (SBMs) and propose a “corrected Bayesian information criterion” (CBIC), to determine the number of communities and show that the proposed criterion is consistent under mild conditions as the size of the network and the number of communities go to infinity. The CBIC outperforms those used in Wang and Bickel and Saldana, Yu, and Feng which tend to underestimate and overestimate the number of communities, respectively. The results are further extended to degree corrected SBMs. Numerical studies demonstrate our theoretical results.

Date: 2020
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DOI: 10.1080/01621459.2019.1637744

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