Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization

doi:10.1109/TNSE.2020.3040407

	Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization
	Luo, Xin 2,3,4; Liu, Zhigang 1,2,4; Shang, Mingsheng 2,3,4; Lou, Jungang 6; Zhou, MengChu 5
刊名	IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
	2021
卷号	8 期号:1 页码:463-476
关键词	Detectors Symmetric matrices Image edge detection Social networking (online) Topology Measurement Knowledge engineering Computational Intelligence Social Network Network Representation Community Detection Pointwise Mutual Information Symmetric and Non-negative Matrix Factorization Graph-regularization
ISSN号	2327-4697
DOI	10.1109/TNSE.2020.3040407
通讯作者	Lou, Jungang(loujungang0210@hotmail.com) ; Zhou, MengChu(zhou@njit.edu)
英文摘要	Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle this task. However, existing SNMF models mostly focus on a network's first-order topological information described by its adjacency matrix without considering the implicit associations among involved nodes. To address this issue, this study proposes a Pointwise mutual information-incorporated and Graph-regularized SNMF (PGS) model. It uses a) Pointwise Mutual Information to quantify implicit associations among nodes, thereby completing the missing but crucial information among critical nodes in a uniform way; b) graph-regularization to achieve precise representation of local topology, and c) SNMF to implement efficient community detection. Empirical studies on eight real-world social networks generated by industrial applications demonstrate that a PGS model achieves significantly higher accuracy gain in community detection than state-of-the-art community detectors.
资助项目	National Natural Science Foundation of China[61772493] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyj-msxmX0578] ; Natural Science Foundation of Zhejiang Province[LR20F020002] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向	Engineering ; Mathematics
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000631202700037
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/13155]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Lou, Jungang; Zhou, MengChu
作者单位	1.Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China 2.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 3.Univ Chinese Acad Sci, Chongqing Sch, Chongqing 400714, Peoples R China 4.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 5.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 6.Huzhou Univ, Zhejiang Prov Key Lab Smart Management & Applicat, Huzhou 313000, Peoples R China
推荐引用方式 GB/T 7714	Luo, Xin,Liu, Zhigang,Shang, Mingsheng,et al. Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization[J]. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING,2021,8(1):463-476.
APA	Luo, Xin,Liu, Zhigang,Shang, Mingsheng,Lou, Jungang,&Zhou, MengChu.(2021).Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization.IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING,8(1),463-476.
MLA	Luo, Xin,et al."Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization".IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 8.1(2021):463-476.