Using Maximum Entry-Wise Deviation to Test the Goodness of Fit for Stochastic Block Models

Jianwei Hu, Jingfei Zhang, Hong Qin, Ting Yan, Ji Zhu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Abstract–The stochastic block model is widely used for detecting community structures in network data. How to test the goodness of fit of the model is one of the fundamental problems and has gained growing interests in recent years. In this article, we propose a novel goodness-of-fit test based on the maximum entry of the centered and rescaled adjacency matrix for the stochastic block model. One noticeable advantage of the proposed test is that the number of communities can be allowed to grow linearly with the number of nodes ignoring a logarithmic factor. We prove that the null distribution of the test statistic converges in distribution to a Gumbel distribution, and we show that both the number of communities and the membership vector can be tested via the proposed method. Furthermore, we show that the proposed test has asymptotic power guarantee against a class of alternatives. We also demonstrate that the proposed method can be extended to the degree-corrected stochastic block model. Both simulation studies and real-world data examples indicate that the proposed method works well. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1373-1382
Number of pages10
JournalJournal of the American Statistical Association
Volume116
Issue number535
DOIs
StatePublished - 2021

Keywords

  • Community detection
  • Degree-corrected stochastic block model
  • Goodness-of-fit test
  • Network data
  • Stochastic block model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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