ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware Embedding
Heterogeneous graph neural networks (HGNs) have attracted more and more attention recently due to their wide applications such as node classification, community detection, and recommendation. Significant progress has been made by graph neural networks. However, we argue that attention mechanisms tha...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10918934/ |
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