Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding
This work addresses the complexity in modeling heterogeneous graphs for researchers and practitioners, offering a more efficient alternative to existing approaches.
The paper tackles the problem of enabling homogeneous Graph Neural Networks (GNNs) to process heterogeneous graphs by proposing a simple framework called RE-GNN, which uses relation embeddings and achieves competitive performance with improved efficiency compared to existing methods.
Graph Neural Networks (GNNs) have been generalized to process the heterogeneous graphs by various approaches. Unfortunately, these approaches usually model the heterogeneity via various complicated modules. This paper aims to propose a simple yet effective framework to assign adequate ability to the homogeneous GNNs to handle the heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Network (RE-GNN), which employs only one parameter per relation to embed the importance of distinct types of relations and node-type-specific self-loop connections. To optimize these relation embeddings and the model parameters simultaneously, a gradient scaling factor is proposed to constrain the embeddings to converge to suitable values. Besides, we interpret the proposed RE-GNN from two perspectives, and theoretically demonstrate that our RE-GCN possesses more expressive power than GTN (which is a typical heterogeneous GNN, and it can generate meta-paths adaptively). Extensive experiments demonstrate that our RE-GNN can effectively and efficiently handle the heterogeneous graphs and can be applied to various homogeneous GNNs.