HGNN-GAMS: Heterogeneous Graph Neural Networks for Graph Attribute Mining and Semantic Fusion
Heterogeneous Graph Neural Networks (HGNNs) have attracted significant research attention in recent years due to their ability to capture complex interactions among various node types in heterogeneous graphs (HGs). However, existing methods face critical challenges, including the loss of graph attri...
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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Assuntos: | |
| Acceso en liña: | https://ieeexplore.ieee.org/document/10804165/ |
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