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  • 统计与管理学院2018年学术报告第8期

    【主 题】 Pairwise Covariates-Adjusted Block Model for Community Detection

    【报告人】 Yang Feng 副教授

    Columbia University

    【时 间】 2018年03月15日(星期四)16:00-17:00

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    摘 要】One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is one widely used model for network data with different estimation methods developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all nodes in the same community are stochastically equivalent, which may not be suitable for practical applications. We introduce pairwise covariates-adjusted stochastic block model (PCABM), a generalization of SBM that incorporates pairwise covariate information. In our model, the pairwise covariates can be constructed using any bivariate function of the corresponding covariates of the pair of nodes considered. We study the maximum likelihood estimators of the coefficients for the covariates as well as the community assignments. It is shown that both the coefficient estimates of the covariates and the community assignments are consistent under typical sparsity conditions. Spectral clustering with adjustment (SCWA) is introduced to efficiently solve PCABM. Under certain conditions, we derive the error bound of community estimation under SCWA and show that it is community detection consistent. PCABM compares favorably with SBM or degree-corrected stochastic block model (DCBM) under a wide range of simulated and real networks when covariate information is accessible. This is a joint work with Sihan Huang.

    嘉宾简介http://www.stat.columbia.edu/~yangfeng/