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Learning specific and conserved features of multi-layer networks

文献类型：期刊论文

作者Wu, Wenming3; Yang, Tao2; Ma, Xiaoke3; Zhang, Wensheng1; Li, He3; Huang, Jianbin3; Li, Yanni3; Cui, Jiangtao3
刊名INFORMATION SCIENCES
出版日期2023-04-01
卷号622页码:930-945
关键词Multi-layer networks Matrix factorization Graph clustering Joint learning
ISSN号0020-0255
DOI10.1016/j.ins.2022.11.150
通讯作者Ma, Xiaoke(xkma@xidian.edu.cn)
英文摘要Complex systems are composed of multiple types of interactions, where each type of inter-action is encoded in a layer, resulting in multi-layer networks. Detecting layer-specific modules in multi-layer networks are for revealing the functions and structure of systems. However, current algorithms are criticized for failing to quantify and balance the specificity and connectivity of communities in multi-layer networks, resulting in undesirable perfor-mance. To address these problems, we propose a joint Learning Specific and Conserved fea-tures for Clustering in multi-layer networks (called LSCC), where features of vertices simultaneously characterize the shared and layer-specific structure of networks. Specifically, LSCC jointly factorizes multi-layer networks by projecting all layers into a common subspace with nonnegative matrix factorization, where the structure of various layers is represented. Then, LSCC decomposes features of vertices into the conserved and specific parts, where the specificity of vertices of each layer is explicitly quantified. To bal-ance the specificity and connectivity of modules, LSCC joint learns feature extraction and subspace clustering, which is formulated as an optimization problem. The experimental results on 8 datasets demonstrate that the proposed algorithm significantly outperforms the baselines on various measurements.(c) 2022 Elsevier Inc. All rights reserved.
WOS关键词COMMUNITY STRUCTURE ; ALGORITHM
资助项目National Key R & D Program of China[2017YFE0104100] ; National Natural Science Foundation of China[62272361] ; National Natural Science Foundation of China[U22A20345] ; Shaanxi Natural Science Funds for Distinguished Young Scholar[2022JC-38] ; Key Research and Development Program of Shaanxi[2021ZDLGY02-02]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000900836600014
出版者ELSEVIER SCIENCE INC
资助机构National Key R & D Program of China ; National Natural Science Foundation of China ; Shaanxi Natural Science Funds for Distinguished Young Scholar ; Key Research and Development Program of Shaanxi
源URL[http://ir.ia.ac.cn/handle/173211/51162]  
专题多模态人工智能系统全国重点实验室
通讯作者Ma, Xiaoke
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Liaoning Tech Univ, Coll Business Adm, 188 Longwan South Rd, Huludao, Liaoning, Peoples R China
3.Xidian Univ, Sch Comp Sci & Technol, 2 South Taibai Rd, Xian, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Wenming,Yang, Tao,Ma, Xiaoke,et al. Learning specific and conserved features of multi-layer networks[J]. INFORMATION SCIENCES,2023,622:930-945.
APA Wu, Wenming.,Yang, Tao.,Ma, Xiaoke.,Zhang, Wensheng.,Li, He.,...&Cui, Jiangtao.(2023).Learning specific and conserved features of multi-layer networks.INFORMATION SCIENCES,622,930-945.
MLA Wu, Wenming,et al."Learning specific and conserved features of multi-layer networks".INFORMATION SCIENCES 622(2023):930-945.

入库方式： OAI收割

来源：自动化研究所

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