勷勤数学•专家报告

题      目：Two-way Node Popularity Model for Directed and Bipartite Networks

报  告  人：王江洲 助理教授  (邀请人：张旭)

                                                深圳大学

时      间： 11月9日  10：00-11：00

地     点：数科院东楼二楼会议室

报告人简介:

        王江洲，深圳大学数学科学学院，统计与数据科学系助理教授。主要研究方向：大规模网络数据的统计分析、大规模相依数据的多重检验、机器学习和深度学习等与统计学的交叉。目前在统计学领域期刊发表SCI论文十余篇，其中包括：JASA、JCGS、JMVA、CSDA、Computational Statistics 和 Stat等国际期刊。主持科研项目：国家自然科学基金青年项目1项、广东省自然科学基金面上项目1项、中国博士后科学基金面上项目和特别资助（站中）项目各1项、参与面上项目1项。入选深圳市“鹏城孔雀计划”特聘岗位C岗。曾多次受邀在ICSA等国际会议上做报告，并担任JMLR, AOAS, Sinica, JCGS, CSDA和SII等期刊的审稿人。

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.

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