EHG: efficient heterogeneous graph transformer for multiclass node classification

Graph neural networks empowered by the Transformer's self-attention mechanism have arisen as a preferred solution for many graph classification and prediction tasks. Despite their efficacy, these networks are often hampered by their quadratic computational complexity and large model size, which pose significant challenges during graph training and inference. In this study, we present an innovative approach to heterogeneous graph transformation that adeptly navigates these limitations by capturing the rich diversity and semantic depth of graphs with various node and edge types. Our method, which streamlines the key-value interaction to a straightforward linear layer operation, maintains the same level of ranking accuracy while significantly reducing computational overhead and accelerating model training. We introduce the "EHG" model, a testament to our approach's efficacy, showcasing remarkable performance in multiclass node classification on heterogeneous graphs. Our model's evaluation on the DBLP, ACM, OGBN-MAG, and OAG datasets reveals its superiority over existing heterogeneous graph models under identical hyperparameter configurations. Notably, our model achieves a reduction of approximately 25% in parameter count and nearly 20% savings in training time compared to the leading heterogeneous graph-transformer models.