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 ...

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 ...

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

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

Graph contrastive learning aims to achieve an effective measurement of the similarity and dissimilarity between graphs G 1 and G 2 by learning a mapping function f ⋅. For a given pair of graphs G 1 = ...

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