Knowledge Commons of Institute of Automation,CAS
Learning specific and conserved features of multi-layer networks | |
Wu, Wenming1; Yang, Tao2; Ma, Xiaoke1; Zhang, Wensheng3; Li, He1; Huang, Jianbin1; Li, Yanni1; Cui, Jiangtao1 | |
发表期刊 | INFORMATION SCIENCES |
ISSN | 0020-0255 |
2023-04-01 | |
卷号 | 622页码:930-945 |
通讯作者 | Ma, Xiaoke(xkma@xidian.edu.cn) |
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. |
关键词 | Multi-layer networks Matrix factorization Graph clustering Joint learning |
DOI | 10.1016/j.ins.2022.11.150 |
关键词[WOS] | COMMUNITY STRUCTURE ; ALGORITHM |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | 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 |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Information Systems |
WOS记录号 | WOS:000900836600014 |
出版者 | ELSEVIER SCIENCE INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51163 |
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.Xidian Univ, Sch Comp Sci & Technol, 2 South Taibai Rd, Xian, Shaanxi, Peoples R China 2.Liaoning Tech Univ, Coll Business Adm, 188 Longwan South Rd, Huludao, Liaoning, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing, 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. |
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