Learning specific and conserved features of multi-layer networks

doi:10.1016/j.ins.2022.11.150

	Learning specific and conserved features of multi-layer networks
	Wu, Wenming 1; Yang, Tao 2; Ma, Xiaoke 1; Zhang, Wensheng3 ; Li, He 1; Huang, Jianbin 1; Li, Yanni 1; Cui, Jiangtao 1
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.