Hongmin Sun

Contrastive Auxiliary Learning with Structure Transformation for Heterogeneous Graphs

In recent years, methods based on heterogeneous graph neural networks (HGNNs) have been widely used for embedding heterogeneous graphs (HGs) due to their ability to effectively encode the rich information from HGs into low-dimensional node embeddings. Existing HGNNs focus on neighbor aggregation and…