学术报告

题      目：Joint Community Detection in Random Effects Stochastic Block Models via the Split-likelihood Method

报  告  人：王江洲  副教授  (邀请人：吴琴 )

                                    深圳大学

时      间：5月11日  17：15-16：00

地     点：数学科学学院西楼二楼会议室

报告人简介:

        王江洲，深圳大学统计系助理教授；东北师范大学统计学博士，南方科技大学博士后。主要研究方向：大规模网络数据的统计分析、大规模相依数据的多重检验；目前在统计学领域期刊发表SCI论文6篇，其中包括：Journal of the American Statistical Association（统计学四大顶级期刊之一）、Computational Statistics and Data Analysis 和 Stat等国际知名期刊。主持：中国博士后科学基金面上项目1项（8万）、中国博士后科学基金特别资助（站中）1项（18万），青年基金(30万)。曾多次受邀在ICSA国际会议上做报告。

摘      要：

        In this study, we tackle the joint community detection in multi-layer networks under a random effects stochastic block model. This model presents a unique challenge as it induces variability in the community structure across each layer of the multi-layer network. This variability is a random transformation originating from a common community structure that permeates all layers. The exact fit for this model is an NP-hard problem. We propose a solution, the ‘split-likelihood method’, which balances detection accuracy and computational efficiency. It employs an approximate likelihood maximization process by decoupling the row and column labels of community assignment. We further establish the convergence theory for our proposed method, along with the consistency theories for the estimated community labels derived from it. Extensive numerical results suggest that our method excels in both detection accuracy and computational efficiency. Finally, we conducted a resting state fMRI study on schizophrenia, to demonstrate the practical applicability of the proposed method.

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