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哥伦比亚大学 冯阳副教授:Are there any community structure in a hypergraph?

([西财新闻] 发布于 :2019-01-07 )

光华讲坛——社会名流与企业家论坛第 5203 期

 

主題:Are there any community structure in a hypergraph?

主講人:哥伦比亚大学 冯阳副教授

主持人:统计学院 常晋源教授

時間:2019年1月9日(星期三)15:00-16:00

地點:西南財經大學光华校区光华楼1007会议室

主辦單位:数据科学与商业智能联合实验室 统计学院 科研处

 

主講人簡介:

Yang Feng is an associate professor of statistics at Columbia University. In 2010, he got his Ph.D. in Operations Research & Financial Engineering from Princeton University under the supervision of Professor Jianqing Fan. His current research interest includes high-dimensional statistical learning, network models, nonparametric and semiparametric methods, and bioinformatics. He is currently an associate editor for Journal of Business & Economic Statistics, Statistica Sinica, Computational Statistics & Data Analysis, and Statistical Analysis and Data Mining: The ASA Data Science Journal. His research is partially supported by NSF CAREER Grant DMS-1554804.

內容摘要:

Many complex networks in the real world can be formulated as hypergraphs where community detection has been widely used. However, the fundamental question of whether communities exist or not in an observed hypergraph still remains unresolved. The aim of the present paper is to tackle this important problem. Specifically, we study when a hypergraph with community structure can be successfully distinguished from its Erd\"{o}s-R\'{e}nyi counterpart, and propose concrete test statistics based on hypergraph cycles when the models are distinguishable. Our contributions are summarized as follows. For uniform hypergraphs, we show that successful testing is always impossible when average degree tends to zero, might be possible when the average degree is bounded, and is possible when the average degree is growing. We obtain asymptotic distributions of the proposed test statistics and analyze their power. Our results for growing degree case are further extended to nonuniform hypergraphs in which a new test involving both edge and hyperedge information is proposed. The novel aspect of our new test is that it is provably more powerful than the classic test involving only edge information. Simulation and real data analysis support our theoretical findings.


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