Distance Information Improves Heterogeneous Graph Neural Networks

Heterogeneous graph neural network (HGNN) has shown superior performance and attracted considerable research interest. However, HGNN inherits the limitation of expressive power from GNN via learning $individual$ node embeddings based on their structural neighbors, largely ignoring the potential correlations between nodes and leading to sub-optimal performance. How to establish correlations among multiple node embeddings and improve the expressive power of HGNN is still an open problem. To solve the above problem, we propose a simple and effective technique called heterogeneous distance encoding (HDE) to fundamentally improve the expressive power of HGNN. Specifically, we define heterogeneous shortest path distance to describe the relative distance between nodes, and then jointly encode such distances for multiple nodes of interest to establish their correlation. By simply injecting the encoded correlation into the neighbor aggregating process, we can learn more expressive heterogeneous graph representations for downstream tasks. More importantly, the proposed HDE relies only on the graph structure and ensures the inductive ability of HGNN. We also propose an efficient HDE algorithm that can significantly reduce the computational overhead. Significant improvements on both transductive and inductive tasks over four real-world graphs demonstrate the effectiveness of HDE in improving the expressive power of HGNN.