Symmetry and Nonnegativity-Constrained Matrix Factorization for Community Detection

作     者：Zhigang Liu Guangxiao Yuan Xin Luo Zhigang Liu;Guangxiao Yuan;Xin Luo

作者机构：the School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Faculty of Liberal Arts and Social SciencesThe Education University of Hong KongHong Kong 999077China the School of Computer Science and TechnologyDongguan University of TechnologyDongguan 523808 the College of Computer and Information ScienceSouthwest UniversityChongqing 400715China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2022年第9卷第9期

页      面：1691-1693页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：the CAAI-Huawei Mind Spore Open Fund(CAAIXSJLJJ-2021-035A) the Doctoral Student Talent Training Program of Chongqing University of Posts and Telecommunications(BYJS202009) 

主　　题：network editor networks 

摘      要：Dear editor,This letter presents a novel symmetry and nonnegativity-constrained matrix factorization(SNCMF)-based community detection model on undirected networks such as a social *** is a fundamental characteristic of a network,making community detection a vital yet thorny issue in network *** to its high interpretability and scalability。