Learning specific and conserved features of multi-layer networks

doi:10.1016/j.ins.2022.11.150

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Learning specific and conserved features of multi-layer networks
	Wu, Wenming 3; Yang, Tao 2; Ma, Xiaoke 3; Zhang, Wensheng 1; Li, He 3; Huang, Jianbin 3; Li, Yanni 3; Cui, Jiangtao 3
刊名	INFORMATION SCIENCES
	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
DOI	10.1016/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.
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关键词	COMMUNITY STRUCTURE ; ALGORITHM
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000900836600014
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
内容类型	期刊论文
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.