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Multi-Constrained Embedding for Accurate Community Detection on Undirected Networks

文献类型：期刊论文

作者Wang, Qingxian3; Liu, Xinyu3; Shang, Tianqi2; Liu, Zhigang1; Yang, Han3; Luo, Xin1
刊名IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
出版日期2022-09-01
卷号9期号:5页码:3675-3690
关键词Symmetric matrices Symbols Matrix decomposition Context modeling Task analysis Periodic structures Electronic mail Community detection network embedding non-negative matrix factorization non-negative model and alternating direction method of multipliers
ISSN号2327-4697
DOI10.1109/TNSE.2022.3176062
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要A Symmetric Non-negative Matrix Factorization (SNMF)-based network embedding model adopts a unique Latent Factor (LF) matrix for describing the symmetry of an undirected network, which reduces its representation ability to the target network and thus resulting in accuracy loss when performing community detection. To address this issue, this paper proposes a new undirected network embedding model, i.e., Alternating Direction Method of Multipliers (ADMM)-based, Modularity, Symmetry and Nonnegativity-constrained Embedding (AMSNE), which can be applicable to undirected, weighted or unweighted networks. It relies on two-fold ideas: a) Introducing the symmetry constraints into the model to correctly describe the symmetric of an undirected network without accuracy loss; and b) Adopting the ADMM principle to efficiently solve its constrained objective. Extensive experiments on eight real-world networks strongly evidence that the proposed AMSNE outperform several state-of-the-art models, making it suitable for real applications.
资助项目National Natural Science Foundation of China[62072429] ; Major Science and Technology Projects of Sichuan Province[2018GZDZX0042] ; Major Science and Technology Projects of Sichuan Province[2020YFG0021] ; Sichuan Science and Technology Program[2021YFG0305] ; Sichuan Science and Technology Program[2022YFG0175] ; Fundamental Research Funds for the Central Universities[ZYGX2020ZB021] ; Fundamental Research Funds for the Central Universities[CAAIXSJLJJ2021-035A] ; Intelligent Terminal Key Laboratory of Sichuan Province[SCITLAB-0003] ; Intelligent Terminal Key Laboratory of Sichuan Province[SCITLAB-1003]
WOS研究方向Engineering ; Mathematics
语种英语
WOS记录号WOS:000852246800058
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.138/handle/2HOD01W0/16401]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Luo, Xin
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.Sichuan Univ, Chengdu 610207, Peoples R China
3.Univ Elect Sci & Technol China, Chengdu 610054, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Qingxian,Liu, Xinyu,Shang, Tianqi,et al. Multi-Constrained Embedding for Accurate Community Detection on Undirected Networks[J]. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING,2022,9(5):3675-3690.
APA Wang, Qingxian,Liu, Xinyu,Shang, Tianqi,Liu, Zhigang,Yang, Han,&Luo, Xin.(2022).Multi-Constrained Embedding for Accurate Community Detection on Undirected Networks.IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING,9(5),3675-3690.
MLA Wang, Qingxian,et al."Multi-Constrained Embedding for Accurate Community Detection on Undirected Networks".IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 9.5(2022):3675-3690.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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