Heterogeneous Graph Neural Network With Multi-View Representation Learning

doi:10.1109/TKDE.2022.3224193

	Heterogeneous Graph Neural Network With Multi-View Representation Learning
	Shao, Zezhi 1,2; Xu, Yongjun 1; Wei, Wei 3; Wang, Fei 1; Zhang, Zhao 1; Zhu, Feida 4
	2023-11-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	35 期号:11 页码:11476-11488
摘要	In recent years, graph neural networks (GNNs)-based methods have been widely adopted for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich information from a HG into the low-dimensional node embeddings. However, previous works usually easily fail to fully leverage the inherent heterogeneity and rich semantics contained in the complex local structures of HGs. On the one hand, most of the existing methods either inadequately model the local structure under specific semantics, or neglect the heterogeneity when aggregating information from the local structure. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain node embeddings with versatility. To address the problem, we propose a Heterogeneous Graph Neural Network for HG embedding within a Multi-View representation learning framework (named MV-HetGNN), which consists of a view-specific ego graph encoder and auto multi-view fusion layer. MV-HetGNN thoroughly learns complex heterogeneity and semantics in the local structure to generate comprehensive and versatile node representations for HGs. Extensive experiments on three real-world HG datasets demonstrate the significant superiority of our proposed MV-HetGNN compared to the state-of-the-art baselines in various downstream tasks, e.g., node classification, node clustering, and link prediction.
关键词	Heterogeneous graphs graph neural networks graph embedding
DOI	10.1109/TKDE.2022.3224193
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61902376] ; National Natural Science Foundation of China[61902382] ; National Natural Science Foundation of China[62276110] ; CCF-AFSG Research Fund[RF20210005] ; fund of Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL) ; China Post-doctoral Science Foundation[2021M703273]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001089176900038
出版者	IEEE COMPUTER SOC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38106
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wei, Wei; Wang, Fei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Hubei, Peoples R China 4.Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore
推荐引用方式 GB/T 7714	Shao, Zezhi,Xu, Yongjun,Wei, Wei,et al. Heterogeneous Graph Neural Network With Multi-View Representation Learning[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(11):11476-11488.
APA	Shao, Zezhi,Xu, Yongjun,Wei, Wei,Wang, Fei,Zhang, Zhao,&Zhu, Feida.(2023).Heterogeneous Graph Neural Network With Multi-View Representation Learning.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(11),11476-11488.
MLA	Shao, Zezhi,et al."Heterogeneous Graph Neural Network With Multi-View Representation Learning".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.11(2023):11476-11488.