Two-way Node Popularity Model for Directed and Bipartite Networks

作     者：Jing, Bing-Yi Li, Ting Wang, Jiangzhou Wang, Ya 

作者机构：Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong999077 Hong Kong School of Mathematical Sciences Institute of Statistical Sciences Shenzhen University Shenzhen518060 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Computational efficiency 

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. Copyright © 2024, The Authors. All rights reserved.