【数学与统计及交叉学科前沿论坛------高端学术讲座第119场】

报告题目：Joint community detection in random effects stochastic block models via the split-likelihood method

报告人：刘秉辉教授 东北师范大学

报告时间：2024年9月26日（周四）15:30-16:30

报告地点：腾讯会议ID：308-913-440

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.

报告人简介：刘秉辉，东北师范大学，教授，统计系主任；主要研究方向为统计机器学习和网络数据分析；在统计学、计算机&人工智能、计量经济学领域期刊发表SCI论文三十余篇，部分成果发表在：Journal of the American Statistical Association、Annals of Statistics、Annals of Applied Statistics、Artificial Intelligence、Journal of Machine Learning Research、Journal of Econometrics、Journal of Business & Economic Statistics等权威期刊；入选教育部青年长江学者、国家天元数学东北中心优秀青年学者、吉林省拔尖创新人才；担任中国现场统计研究会因果推断分会、统计交叉科学研究分会副理事长。