[Lecture] Feng Yang: Testing Community Structure for Hypergraphs（超图的社区结构检测）
Time: 10:00-11:00am, Sep. 29th, 2020, Tuesday
Venue: RoomA1514, Science Building, North Zhongshan Road Campus
Spreker: Yang Feng, Associate Professor of Biostatistics at New York University
Abstract: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 remains unclear. The aim of the work is to tackle this important problem. Specifically, we systematically study when a hypergraph with community structure can be successfully distinguished from its Erdos-Renyi counterpart, and propose concrete test statistics based on hypergraph cycles when the models are distinguishable. For uniform hypergraphs, we show that the success of hypergraph testing highly depends on the order of the average degree as well as the signal to noise ratio. In addition, we obtain asymptotic distributions of the proposed test statistics and analyze their power. Our results are further extended to nonuniform hypergraphs in which a new test involving both edge and hyperedge information is proposed. The novel aspect of our 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.
Speaker’s Bio:Yang Feng is an associate professor of biostatistics in the School of Global Public Health at New York University. Feng focuses on developing and applying machine learning methods in public health, high-dimensional data analysis, network models, nonparametric and semiparametric methods, and bioinformatics. He has published over 30 articles in journals including the Annals of Statistics, JASA, JRSSB, JMLR, Journal of Econometrics, IEEE-PAMI, and Science Advances. He is currently an associate editor for the Journal of Business & Economic Statistics, Statistica Sinica, and Statistical Analysis and Data Mining: The ASA Data Science Journal. His research is partially supported by NSF CAREER Grant DMS-2013789.
