Dear editor, This letter presents a novel symmetry and nonnegativity-constr-ained 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,a symmetric nonnegative matrix factorization(SNMF)model is frequently adopted to address this issue.However,it adopts a unique latent factor(LF)matrix for representing an undirected network's symmetry,which leads to a reduced latent space that impairs its representation learning ability.Motivated by this discovery,the proposed SNCMF model innovatively adopts the following three-fold ideas:1)Leveraging multiple LF matrices to represent a network,thereby enhancing its representation learning ability;2)Introducing a symmetry regulari-zation term that implies the equality constraint between multiple LF matrices to illustrate the network's symmetry;and 3)Incorporating graph regularization into the model to preserve the network's intrinsic geometry.Experimental results on several real-world networks indicate that the proposed SNCMF-based community detector outperforms the benchmark and state-of-the-art models in achieving highly-accurate community detection results.

Zhigang Liu;Guangxiao Yuan;Xin Luo

School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065;Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,China;Faculty of Liberal Arts and Social Sciences,The Education University of Hong Kong,Hong Kong 999077,China;School of Computer Science and Technology,Dongguan University of Technology,Dongguan 523808,and also with the College of Computer and Information Science,Southwest University,Chongqing 400715,China

自动化学报（英文版）

[1]Zhigang Liu;Guangxiao Yuan;Xin Luo-.Symmetry and Nonnegativity-Constrained Matrix Factorization for Community Detection)[J].自动化学报（英文版）,2022(09):1691-1693

Nonnegativity,nonnegativity,constr,ained,SNCMF,regulari

Symmetry,Constrained,Matrix,Factorization,Community,Detection,Dear,editor, This,letter,presents,novel,symmetry,matrix,factorization,community,detection,undirected,networks,such,social,fundamental,characteristic,making,vital,yet,thorny,issue,representation,Owing,its,interpretability,scalability,symmetric,nonnegative,SNMF,frequently,adopted,address,this,However,adopts,unique,latent,LF,representing,which,leads,reduced,space,that,impairs,learning,Motivated,discovery,proposed,innovatively,following,three,fold,ideas,Leveraging,multiple,matrices,thereby,enhancing,Introducing,term,implies,equality,constraint,between,illustrate,Incorporating,graph,regularization,into,preserve,intrinsic,geometry,Experimental,results,several,real,world,indicate,detector,outperforms,benchmark,state,art,models,achieving,highly,accurate

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