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.

提出一种新的变压器体系结构，名为Graph Propagation Transformer(GPTrans)，利用一种名为Graph Propagation Attention(GPA)的新注意机制，在构建变压器块中的注意模块时，充分考虑了图形中节点和边的信息传递，并在多个基准数据集上验证了其优于许多先进的变压器的性能。

图传播变压器用于图表示学习