Megnn: Meta-path extracted graph neural network for heterogeneous graph representation learning

Abstract

Heterogeneous graphs with multiple types of nodes and edges are ubiquitous in the real world and possess immense value in many graph-based downstream applications. However, the heterogeneity within nodes and edges in heterogeneous graphs has brought pressing challenges for practical node representation learning. Existing works manually define multiple meta-paths to model the semantic relationships in heterogeneous graphs. Such strategies heavily rely on the quality of domain knowledge and require extensive hand-crafted works. In this paper, we propose a novel Meta-path Extracted heterogeneous Graph Neural Network (Megnn) that is capable of extracting meaningful meta-paths in heterogeneous graphs, providing insights about data and explainable conclusions to the model’s effectiveness. Concretely, Megnn leverages heterogeneous convolution to combine different bipartite sub-graphs corresponding to edge types into a new trainable graph structure. By adopting the message passing paradigm of GNNs through trainable convolved graphs, Megnn can optimize and extract effective meta-paths for heterogeneous graph representation learning. To enhance the robustness of Megnn, we leverage multiple channels to yield various graph structures and devise a channel consistency regularizer to enforce the node embeddings learned from different channels to be similar. Extensive experimental results on three datasets not only show the effectiveness of Megnn compared with the state-of-the-art methods, but also demonstrate the favorable interpretability of the extracted meta-paths.