Hyperbolic Heterogeneous Graph Attention Networks
Pages 561 - 564
Abstract
Most previous heterogeneous graph embedding models represent elements in a heterogeneous graph as vector representations in a low-dimensional Euclidean space. However, because heterogeneous graphs inherently possess complex structures, such as hierarchical or power-law structures, distortions can occur when representing them in Euclidean space. To overcome this limitation, we propose Hyperbolic Heterogeneous Graph Attention Networks (HHGAT) that learn vector representations in hyperbolic spaces with metapath instances. We conducted experiments on three real-world heterogeneous graph datasets, demonstrating that HHGAT outperforms state-of-the-art heterogeneous graph embedding models in node classification and clustering tasks. This superior performance is attributed to HHGAT's ability to capture the complex structure of heterogeneous graphs effectively.
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References
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Index Terms
- Hyperbolic Heterogeneous Graph Attention Networks
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Published In
May 2024
1928 pages
ISBN:9798400701726
DOI:10.1145/3589335
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw,
- Roy Ka-Wei Lee
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Publication History
Published: 13 May 2024
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- Institute of Information & Communications Technology Planning & Evaluation
- National Research Foundation of Korea
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WWW '24
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