Publication Type
Journal Article
Version
publishedVersion
Publication Date
11-2023
Abstract
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.
Keywords
Heterogeneous graphs, Graph neural networks, Graph embedding
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Knowledge and Data Engineering
Volume
35
Issue
11
First Page
11476
Last Page
11488
ISSN
1041-4347
Identifier
10.1109/TKDE.2022.3224193
Publisher
Institute of Electrical and Electronics Engineers
Citation
SHAO, Zezhi; XU, Yongjun; WEI, Wei; WANG, Fei; ZHANG, Zhao; and ZHU, Feida.
Heterogeneous graph neural network with multi-view representation learning. (2023). IEEE Transactions on Knowledge and Data Engineering. 35, (11), 11476-11488.
Available at: https://ink.library.smu.edu.sg/sis_research/8607
Creative Commons License
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