Symmetry and Nonnegativity-Constrained Matrix Factorization for Community Detection  

作　　者：Zhigang Liu Guangxiao Yuan Xin Luo 

机构地区：[1]the School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065 [2]the Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,China [3]the Faculty of Liberal Arts and Social Sciences,The Education University of Hong Kong,Hong Kong 999077,China [4]the School of Computer Science and Technology,Dongguan University of Technology,Dongguan 523808 [5]the College of Computer and Information Science,Southwest University,Chongqing 400715,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第9期1691-1693,共3页自动化学报（英文版）

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)。

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 network.Community is a fundamental characteristic of a network,making community detection a vital yet thorny issue in network representation.Owing to its high interpretability and scalability。

关 键 词：NETWORK EDITOR NETWORKS 

分 类 号：O157.5[理学—数学]