Hypergraph-Enhanced Self-supervised Heterogeneous Graph Representation Learning

Heterogeneous graphs are widely used to model complex systems in the real world, such as social networks, biomedical networks, and citation networks. Learning heterogeneous graph embeddings (i.e., representations) provides a way to perform deep learning-driven downstream tasks, such as recommendation and prediction. However, existing heterogeneous graph neural networks mainly capture pairwise relations in heterogeneous graphs, while real-world relations are often more complex and not limited to pairs. In this paper, we propose a novel method to capture relations beyond pairwise in heterogeneous graphs, namely HHGR. First, we construct hypergraphs from heterogeneous graphs and preserve semantic information of network schema and meta paths. Second, we design a cross-view contrast module to aggregate information on different aspects. Further, to enhance the performance of HHGR, we propose a semantic positive sampling strategy, which chooses proper positive samples according to structure and attribute semantics. Extensive experiments conducted on various real-world datasets demonstrate the state-of-the-art performance of HHGR.