报告题目:Two-way Node Popularity Model for Directed and Bipartite Networks

报告人：王江洲 助理教授 深圳大学

邀请人：段江涛

报告时间：2023年11月15日14:30--16:30

腾讯会议ID：183455902 密码：123456

报告摘要:In recent years, 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). We also introduce the Rank-One Approximation Algorithm (ROA) for model fitting and community structure identification, and provide a comprehensive theoretical analysis. 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.

报告人简介:王江洲，深圳大学数学科学学院统计系助理教授。2021年博士毕业于东北师范大学，师从郭建华教授。随后于2021～2023年在南方科技大学统计与数据科学科学系做博士后，合作导师为荆炳义教授和邵启满教授。主要研究方向为大规模网络数据的统计分析和大规模相依数据的多重检验。目前在统计学领域期刊发表多篇SCI论文，所发期刊包括JASA，CSDA，Comutational Statistics和Stat等。主持国家自然科学青年基金项目、中国博士后科学基金面上项目和中国博士后科学基金特别资助（站中）项目各1项。同时也曾多次受邀在ICSA国际会议上做报告，并担任AOAS, SINICA, CSDA和SII等多个统计学期刊的审稿人。