Graph Propagation Transformer for Graph Representation Learning

• URL: http://arxiv.org/abs/2305.11424v3
• Date: Wed, 09 Oct 2024 04:25:18 GMT
• Title: Graph Propagation Transformer for Graph Representation Learning
• Authors: Zhe Chen, Hao Tan, Tao Wang, Tianrun Shen, Tong Lu, Qiuying Peng, Cheng Cheng, Yue Qi,
• Abstract summary: 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. We show that our method outperforms many state-of-the-art transformer-based graph models with better performance.
• Score: 36.01189696668657
• License:
• Abstract: 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.

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