Multi-aspect self-supervised learning for heterogeneous information network

doi:10.1016/j.knosys.2021.107474

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 智能交互

	Multi-aspect self-supervised learning for heterogeneous information network
	Che, Feihu 1,2; Tao, Jianhua 1,2,3; Yang, Guohua 2; Liu, Tong 2; Zhang, Dawei 2
刊名	KNOWLEDGE-BASED SYSTEMS
	2021-12-05
卷号	233 页码:14
关键词	Heterogeneous information network Self-supervised Contrastive learning Graph neural network
ISSN号	0950-7051
DOI	10.1016/j.knosys.2021.107474
通讯作者	Tao, Jianhua(jhtao@nlpr.ia.ac.cn)
英文摘要	Graph neural networks (GNNs) have made remarkable advancements in processing graph-structured data with all nodes and edges belonging to the same type. However, various types of node and relations exist in heterogeneous information networks (HINs), and due to this, HINs contain rich structural and semantic information. To tackle this heterogeneity, existing methods usually apply several well-designed metapaths to HINs to obtain the corresponding homogeneous subgraphs. However, these methods either fail to capture the interconnections between the same nodes in different subgraphs or require qualified labels. To address these issues, we propose a new multi-aspect self-supervised learning (SSL) framework for HIN representation in an unsupervised manner: (1) we design a new contrastive learning model to capture the similarities between the same nodes in different homogeneous subgraphs, and (2) we maximize the mutual information between the local patches and the global representation in one subgraph. Extensive experiments on various downstream tasks demonstrate the superiority of our model in comparison to the existing state-of-the-art methods. (c) 2021 Elsevier B.V. All rights reserved.
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000709919000012
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46303]
专题	模式识别国家重点实验室_智能交互
通讯作者	Tao, Jianhua
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Che, Feihu,Tao, Jianhua,Yang, Guohua,et al. Multi-aspect self-supervised learning for heterogeneous information network[J]. KNOWLEDGE-BASED SYSTEMS,2021,233:14.
APA	Che, Feihu,Tao, Jianhua,Yang, Guohua,Liu, Tong,&Zhang, Dawei.(2021).Multi-aspect self-supervised learning for heterogeneous information network.KNOWLEDGE-BASED SYSTEMS,233,14.
MLA	Che, Feihu,et al."Multi-aspect self-supervised learning for heterogeneous information network".KNOWLEDGE-BASED SYSTEMS 233(2021):14.