Graph Propagation Transformer for Graph Representation Learning
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917796638621696 |
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| author | Chen, Zhe Tan, Hao Wang, Tao Shen, Tianrun Lu, Tong Peng, Qiuying Cheng, Cheng Qi, Yue |
| author_facet | Chen, Zhe Tan, Hao Wang, Tao Shen, Tianrun Lu, Tong Peng, Qiuying Cheng, Cheng Qi, Yue |
| contents | This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks. Specifically, we propose a new attention mechanism called Graph Propagation Attention (GPA). It explicitly passes the information among nodes and edges in three ways, i.e. node-to-node, node-to-edge, and edge-to-node, which is essential for learning graph-structured data. On this basis, we design an effective transformer architecture named Graph Propagation Transformer (GPTrans) to further help learn graph data. We verify the performance of GPTrans in a wide range of graph learning experiments on several benchmark datasets. These results show that our method outperforms many state-of-the-art transformer-based graph models with better performance. The code will be released at https://github.com/czczup/GPTrans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_11424 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Graph Propagation Transformer for Graph Representation Learning Chen, Zhe Tan, Hao Wang, Tao Shen, Tianrun Lu, Tong Peng, Qiuying Cheng, Cheng Qi, Yue Machine Learning Artificial Intelligence This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks. Specifically, we propose a new attention mechanism called Graph Propagation Attention (GPA). It explicitly passes the information among nodes and edges in three ways, i.e. node-to-node, node-to-edge, and edge-to-node, which is essential for learning graph-structured data. On this basis, we design an effective transformer architecture named Graph Propagation Transformer (GPTrans) to further help learn graph data. We verify the performance of GPTrans in a wide range of graph learning experiments on several benchmark datasets. These results show that our method outperforms many state-of-the-art transformer-based graph models with better performance. The code will be released at https://github.com/czczup/GPTrans. |
| title | Graph Propagation Transformer for Graph Representation Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2305.11424 |