Dual-branch graph Transformer for node classification

As an emerging architecture, graph Transformers (GTs) have demonstrated significant potential in various graph-related tasks. Existing GTs are mainly oriented to graph-level tasks and have proved their advantages, but they do not perform well in node classification tasks. This mainly comes from two aspects: (1) The global attention mechanism causes the computational complexity to grow quadratically with the number of nodes, resulting in substantial resource demands, especially on large-scale graphs; (2) a large number of long-distance irrelevant nodes disperse the attention weights and weaken the focus on local neighborhoods. To address these issues, we proposed a new model, dual-branch graph Transformer (DCAFormer). The model divided the graph into clusters with the same number of nodes by a graph partitioning algorithm to reduce the number of input nodes. Subsequently, the original graph was processed by graph neural network (GNN) to obtain outputs containing structural information. Next, we adopted a dual-branch architecture: The local branch (intracluster Transformer) captured local information within each cluster, reducing the impact of long-distance irrelevant nodes on attention; the global branch (intercluster Transformer) captured global interactions across clusters. Meanwhile, we designed a hybrid feature mechanism that integrated original features with GNN outputs and separately optimized the construction of the query ( $Q$), key ( $K$), and value ( $V$) matrices of the intracluster and intercluster Transformers in order to adapt to the different modeling requirements of two branches. We conducted extensive experiments on 8 benchmark node classification datasets, and the results showed that DCAFormer outperformed existing GTs and mainstream GNNs.