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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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10918934/ |
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| author | Xiaoyu Zhu Xinzhe Yu Enze Zha Shiyang Lin |
| author_facet | Xiaoyu Zhu Xinzhe Yu Enze Zha Shiyang Lin |
| author_sort | Xiaoyu Zhu |
| collection | DOAJ |
| description | 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 that focus on computing higher ranking scores for specific types of nodes cannot select the most relevant neighbors for any target node. To enhance the expressiveness of the HGNs, we propose ReAHGN, a Relation-aware embedding for Adaptive Heterogeneous Graph Neural Network. Specifically, we present an adaptive attention mechanism that assigns different weights for each type of target node automatically. In addition, a relation-aware embedding method is adopted to fuse the edge type information effectively. ReAHGN is evaluated for three downstream tasks and on ten public datasets. Experiment results demonstrate that ReAHGN can outperform the state-of-the-art HGNs. |
| format | Article |
| id | doaj-art-394bdc439daa4803bf61ee5459b99b98 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-394bdc439daa4803bf61ee5459b99b982025-08-20T02:55:49ZengIEEEIEEE Access2169-35362025-01-0113449514496210.1109/ACCESS.2025.354992810918934ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware EmbeddingXiaoyu Zhu0Xinzhe Yu1Enze Zha2https://orcid.org/0009-0005-1546-2210Shiyang Lin3https://orcid.org/0000-0003-1498-4226China Offshore Fugro Geosolutions (Shenzhen) Company Ltd., Nanshan, Shenzhen, Guangdong, ChinaChina Offshore Fugro Geosolutions (Shenzhen) Company Ltd., Nanshan, Shenzhen, Guangdong, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, ChinaHeterogeneous 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 that focus on computing higher ranking scores for specific types of nodes cannot select the most relevant neighbors for any target node. To enhance the expressiveness of the HGNs, we propose ReAHGN, a Relation-aware embedding for Adaptive Heterogeneous Graph Neural Network. Specifically, we present an adaptive attention mechanism that assigns different weights for each type of target node automatically. In addition, a relation-aware embedding method is adopted to fuse the edge type information effectively. ReAHGN is evaluated for three downstream tasks and on ten public datasets. Experiment results demonstrate that ReAHGN can outperform the state-of-the-art HGNs.https://ieeexplore.ieee.org/document/10918934/Graph neural networkheterogeneous graph neural networksgraph representation learning |
| spellingShingle | Xiaoyu Zhu Xinzhe Yu Enze Zha Shiyang Lin ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware Embedding IEEE Access Graph neural network heterogeneous graph neural networks graph representation learning |
| title | ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware Embedding |
| title_full | ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware Embedding |
| title_fullStr | ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware Embedding |
| title_full_unstemmed | ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware Embedding |
| title_short | ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware Embedding |
| title_sort | reahgn adaptive heterogeneous graph neural network with relation aware embedding |
| topic | Graph neural network heterogeneous graph neural networks graph representation learning |
| url | https://ieeexplore.ieee.org/document/10918934/ |
| work_keys_str_mv | AT xiaoyuzhu reahgnadaptiveheterogeneousgraphneuralnetworkwithrelationawareembedding AT xinzheyu reahgnadaptiveheterogeneousgraphneuralnetworkwithrelationawareembedding AT enzezha reahgnadaptiveheterogeneousgraphneuralnetworkwithrelationawareembedding AT shiyanglin reahgnadaptiveheterogeneousgraphneuralnetworkwithrelationawareembedding |