Heterogeneous graph neural networks (HGNNs) have demonstrated promising capabilities in addressing various problems defined on heterogeneous graphs containing multiple types of nodes or edges. However ...

Point-of-Interest (POI) recommendation is crucial in the recommendation system field. Graph neural networks are used for POI recommendations, but data sparsity affects GNN training. Existing GNN ...

[WSDM'2023] "HGCL: Heterogeneous Graph Contrastive Learning for Recommendation" collaborative-filtering recommendation graph-neural-networks self-supervised-learning heterogeneous-graph-learning graph ...

Heterogeneous Graph Guided Contrastive Learning for Spatially Resolved Transcriptomics Data (ACM MM24) - hexiao0275/stGCL ...

Researchers introduced X-CLR, a novel contrastive learning method using graph-based sample relationships. This approach ...

RNA molecules exhibit diverse structures and functions, making them promising drug targets. However, predicting RNA-small molecule binding affinity remains challenging due to limited experimental data ...