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Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis

文献类型：期刊论文

作者Luo, Xin1,2,3; Liu, Zhigang1,2,4; Jin, Long1,2,3; Zhou, Yue1,2,4; Zhou, MengChu5,6
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-01-29
页码13
关键词Detectors Convergence Symmetric matrices Social networking (online) Analytical models Tuning Computational modeling Community detection convergence analysis graph regularization nonnegative multiplicative update (NMU) social network analysis symmetric and nonnegative matrix factorization (SNMF)
ISSN号2162-237X
DOI10.1109/TNNLS.2020.3041360
通讯作者Luo, Xin(luoxin21@cigit.ac.cn) ; Zhou, MengChu(zhou@njit.edu)
英文摘要Community detection is a popular yet thorny issue in social network analysis. A symmetric and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative update (NMU) scheme is frequently adopted to address it. Current research mainly focuses on integrating additional information into it without considering the effects of a learning scheme. This study aims to implement highly accurate community detectors via the connections between an SNMF-based community detector's detection accuracy and an NMU scheme's scaling factor. The main idea is to adjust such scaling factor via a linear or nonlinear strategy, thereby innovatively implementing several scaling-factor-adjusted NMU schemes. They are applied to SNMF and graph-regularized SNMF models to achieve four novel SNMF-based community detectors. Theoretical studies indicate that with the proposed schemes and proper hyperparameter settings, each model can: 1) keep its loss function nonincreasing during its training process and 2) converge to a stationary point. Empirical studies on eight social networks show that they achieve significant accuracy gain in community detection over the state-of-the-art community detectors.
资助项目National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Natural Science Foundation of Chongqing (China)[cstc2020jcyjzdxmX0028] ; Chinese Academy of Sciences (CAS) Light of West China Program ; CAAI-Huawei MindSpore Open Fund[CAAIXSJLJJ-2020-004B] ; Chongqing Research Program of Technology Innovation and Application[cstc2019jscxfxydX0027] ; Deanship of Scientific Research (DSR) at King Abdulaziz University[RG-21-135-39] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000732334900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/14952]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Luo, Xin; Zhou, MengChu
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China
3.Univ Chinese Acad Sci, Chongqing Sch, Chongqing 400714, Peoples R China
4.Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China
5.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
6.King Abdulaziz Univ, Ctr Res Excellence Renewable Energy & Power Syst, Jeddah 21589, Saudi Arabia
推荐引用方式
GB/T 7714		 Luo, Xin,Liu, Zhigang,Jin, Long,et al. Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:13.
APA Luo, Xin,Liu, Zhigang,Jin, Long,Zhou, Yue,&Zhou, MengChu.(2021).Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,13.
MLA Luo, Xin,et al."Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):13.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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