Joint community detection in random effects stochastic block models via the split-likelihood method

发布时间：2024-05-16 作者：浏览次数：

Speaker:	刘秉辉	DateTime:	2024年5月18日（周六）上午8：30 - 9：30
Brief Introduction to Speaker: 刘秉辉，东北师范大学，教授、博导，统计系主任；入选国家级青年人才计划、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；主要从事统计机器学习和网络数据分析方面的研究；在统计学、计算机&人工智能、计量经济学领域期刊发表学术论文三十余篇，部分成果发表在：J AM STAT ASSOC、ANN STAT、ANN APPL STAT，ARTIF INTELL、J MACH LEARN RES，J ECONOMETRICS、J BUS ECON STAT等；主持国家自然科学基金项目多项；担任中国现场统计研究会因果推断分会副理事长、中国现场统计研究会统计交叉科学研究分会副理事长等。		Place:	6号楼二楼报告厅
Abstract: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...

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