Edge-Centric Heterogeneous Graph Transformer with Vector-Quantized Memory

Graphs are a powerful medium for solving real-world problems, but graph neural networks (GNNs) face inherent limitations, driving the exploration of graph Transformers. While most existing graph Transformers focus on homogeneous graphs, methods for heterogeneous graphs remain underexplored. Current approaches often overlook edge semantics, essential for capturing diverse patterns, and rely on subgraph sampling, which limits the ability to model global graph patterns effectively. We propose EHFormer, a novel edge-centric heterogeneous graph Transformer. EHFormer encodes relational dependencies using edge-centric sequences and meta-path-aware positional features to capture multi-hop patterns. To address the trade-off between computational efficiency and global context modeling, we introduce a vector-quantized memory mechanism. This mechanism compresses global graph patterns into a compact codebook and empowers integration of global context with local subgraph modeling, enabling EHFormer to encode both local and global patterns. Extensive experiments on benchmark datasets demonstrate that EHFormer achieves state-of-the-art performance, validating its scalability and effectiveness.