Network data has attracted tremendous attention in recent years, and most conventional networks focus on pairwise interactions between two vertices. However, real-life network data may display more complex structures, and multi-way interactions among vertices arise naturally, leading to hypergraph networks. In this talk, we will present a novel method for detecting community structure in general hypergraph networks, uniform or non-uniform. It first introduces a null vertex to augment a non-uniform hypergraph into a uniform multi-hypergraph, and then embeds the multi-hypergraph in a low-dimensional vector space such that vertices within the same community are close to each other. The asymptotic properties of the proposed method will be discussed in terms of both community detection and hypergraph estimation, which are also supported by numerical experiments on some simulated and real examples.
嘉宾简介 About the Speaker
Dr. Junhui Wang is Professor in the School of Data Science at City University of Hong Kong. He received his B.S. in Probability and Statistics from Peking University, and Ph.D. in Statistics from University of Minnesota. Before joining CityU, he was faculty member at Columbia University and University of Illinois at Chicago. His research interests include statistical machine learning and its applications in biomedicine, economics, finance and information technology. He has actively published research articles on leading statistics and machine learning journals, including Journal of American Statistical Association, Biometrika, and Journal of Machine Learning Research. He also serves as Associate Editor of Statistica Sinica, Annals of the Institute of Statistical Mathematics, and Statistics and its interface.