中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 重庆绿色智能技术研究院
  3. 中国科学院重庆绿色智能技术研究院
Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization

文献类型：期刊论文

作者Luo, Xin1,2,3; Liu, Zhigang2,3,4; Shang, Mingsheng1,2,3; Lou, Jungang5; Zhou, MengChu6
刊名IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
出版日期2021
卷号8期号:1页码:463-476
ISSN号2327-4697
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
DOI10.1109/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
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.Univ Chinese Acad Sci, Chongqing Sch, Chongqing 400714, 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.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
4.Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China
5.Huzhou Univ, Zhejiang Prov Key Lab Smart Management & Applicat, Huzhou 313000, Peoples R China
6.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
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.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。