MoHGNN: Enhanced Heterogeneous Graph Neural Network via Metapath Optimization

Abstract

In this paper, we propose a novel heterogeneous graph neural networks (HGNNs) model that addresses two major limitations of existing metapath-based methods: (1) Defining suitable metapaths requires professional knowledge in the special domain. (2) The neighbor nodes of the target node also play crucial roles for embedding, but common methods often overlook this factor. Specifically, our model optimizes metapath selection by evaluating all potential metapaths through edge-based PageRank. Then, through aggregation of intra- and intermetapaths obtain metapath embeddings. Next, we gather neighboring nodes that are highly correlated with the target node and obtain the embeddings of node type to optimize metapath embeddings for the next step. Finally, we employ a two-layer attention architecture to acquire the embedding of target node. The experiments on three real-world datasets demonstrate that our method outperforms state-of-the-art methods in handling downstream tasks such as node classification and clustering.