主题：Two-way Node Popularity Model for Directed and Bipartite Networks
主讲人：李挺 香港理工大学
主持人：刘一鸣 威尼斯欢迎你welcome
时间：2024年9月25日（周三）上午10:00-11:00
地点：威尼斯欢迎你welcome石牌校区教学大楼115室
摘要
There has been extensive research on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world networks. To address this issue, we propose a new probabilistic framework called the Two-Way Node Popularity Model (TNPM). The TNPM also accommodates edges from different distributions within a general sub-Gaussian family. We introduce the Delete-One-Method (DOM) for model fitting and community structure identification, and provide a comprehensive theoretical analysis with novel technical skills dealing with sub-Gaussian generalization. Additionally, we propose the Two-Stage Divided Cosine Algorithm (TSDC) to handle large-scale networks more efficiently. Our proposed methods offer multi-folded advantages in terms of estimation accuracy and computational efficiency, as demonstrated through extensive numerical studies. We apply our methods to two real-world applications, uncovering interesting findings.
主讲人简介
Ting Li is an assistant professor in the Department of Applied Mathematics at Hong Kong Polytechnic University. Prior to joining PolyU, he was a postdoctoral associate in Yale University. He received his PhD in Hong Kong University of Science and Technology. His research focuses on the development of novel statistical learning methods for complex data analysis, including network data analysis, brain data analysis, imaging genetics and genomics. His research papers have appeared in high impact journals and conferences, such as Annals of Statistics, ICML, Genome Research, Human Brain Mapping, Human Genomics, Journal of Business and Economics Statistics and Statistica Sinica.
欢迎感兴趣的师生参加
校对| 刘一鸣
责编| 彭 毅
初审| 姜云卢
终审发布| 何凌云
（来源：威尼斯欢迎你welcome微信公众号）
