中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Heterogeneous Graph Neural Network With Multi-View Representation Learning

文献类型：期刊论文

作者Shao, Zezhi3,4; Xu, Yongjun4; Wei, Wei2; Wang, Fei4; Zhang, Zhao4; Zhu, Feida1
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2023-11-01
卷号35期号:11页码:11476-11488
关键词Heterogeneous graphs graph neural networks graph embedding
ISSN号1041-4347
DOI10.1109/TKDE.2022.3224193
英文摘要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.
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
语种英语
WOS记录号WOS:001089176900038
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/38105]  
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.Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore
2.Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Hubei, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。