One Router to Route Them All: Homogeneous Expert Routing for Heterogeneous Graph Transformers

The introduction of Homogeneous Expert Routing (HER) represents a significant advancement in the field of heterogeneous graph neural networks (HGNNs). Traditional approaches often depend on type-specific experts, which can hinder knowledge transfer across different node types. HER addresses this limitation by stochastically masking type embeddings, encouraging a more flexible, type-agnostic specialization among experts. Evaluated on benchmark datasets such as IMDB, ACM, and DBLP, HER demonstrated superior performance compared to standard Heterogeneous Graph Transformers (HGT) and type-separated Mixture-of-Experts (MoE) baselines. Notably, analysis of the IMDB dataset revealed that HER experts specialized based on semantic patterns, such as movie genres, rather than rigid node types. This shift not only enhances the model's efficiency and interpretability but also establishes a new design principle for heterogeneous graph learning, emphasizing the importance of regularizing type depende…