Graph Transformers have demonstrated superiority in handling complex heterogeneous graphs in recent years. However, existing models still face several inherent challenges: (1) reliance on manually designed meta-paths to encode explicit local graph heterogeneity; (2) inability to capture fine-grained global information from distant yet relevant nodes. To address these limitations, we introduce THFormer, a novel node tokenized heterogeneous graph Transformer that learns expressive node representations by incorporating local and global perspectives. From the local perspective, we employ multiple subsequences for different heterogeneous types to explicitly encode local semantic relations, eliminating the need for manually designed meta-paths. From the global perspective, we design a local masking and global sampling mechanism to construct global structural (semantic) sequences, effectively capturing fine-grained global structural (semantic) information. Subsequently, THFormer separately feeds the resulting global and local sequences into standard Transformer layers as model inputs. Since these sequences represent two distinct views of the same target node, their corresponding outputs are naturally aligned to generate self-supervisory signals for model training, further enhancing the expressiveness and reliability of the target node representation. Extensive experiments are conducted to validate the efficacy of THFormer, and the quantitative performance gains are 0.24%, 0.31%, 0.51%, and 0.81% on DBLP, ACM, IMDB, and Freebase, respectively. The experimental results demonstrate the superiority of THFormer over representative heterogeneous graph neural networks and graph Transformer models.

Label propagation and graph neural networks are two main methods for the semi-supervised node classification problem on graphs. They share similar idea of propagating information over the network, exhibiting promising performance on the node classification task. Despite effectiveness, the limitations of these propagation methods are still not well understood. From the perspective of label propagation then training, we observe three major challenges of these propagation methods. The observations from both theoretical analyses and empirical studies reveal that the propagation operations can degrade performance on certain labels and suffers from structure noise, which is described by edges with two nodes belonging to distinct labels. To address the above issues, we propose a new method termed Robust Label Propagation (RLP). RLP contains two novel strategies, Robust Training and Ada-Mixup. Robust Training can utilize more attribute information to have the overall training correction ability and alleviate the impact of structure noise significantly. Ada-Mixup can help RLP mine useful structure information by integrating information before and after the propagation adaptively. Extensive empirical studies on real-world datasets demonstrate that RLP outperforms the mainstream baselines on the node classification task in terms of effectiveness, efficiency and robustness.