COMMUNITY DETECTION WITH DEPENDENT CONNECTIVITY

• 成果类型: Article
• 署名作者: Yuan, Yubai; Qu, Annie
• 署名单位: University of California System; University of California Irvine
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/20-AOS2042
• 发表日期: 2021
• 页码: 2378-2428
• 关键词: p-asterisk models; functional connectivity; stochastic blockmodels; unknown number; networks; schizophrenia; Consistency; population; guarantees; inference

摘要

In network analysis, within-community members are more likely to be connected than between-community members, which is reflected in that the edges within a community are intercorrelated. However, existing probabilistic models for community detection such as the stochastic block model (SBM) are not designed to capture the dependence among edges. In this paper, we propose a new community detection approach to incorporate intracommunity dependence of connectivities through the Bahadur representation. The proposed method does not require specifying the likelihood function, which could be intractable for correlated binary connectivities. In addition, the proposed method allows for heterogeneity among edges between different communities. In theory, we show that incorporating correlation information can achieve a faster convergence rate compared to the independent SBM, and the proposed algorithm has a lower estimation bias and accelerated convergence compared to the variational EM. Our simulation studies show that the proposed algorithm outperforms the existing multinetwork community detection methods assuming conditional independence among edges. We also demonstrate the application of the proposed method to agricultural product trading networks from different countries and to brain fMRI imaging networks.