To address this issue, we introduce a novel MDA prediction method named MVCL-MDA, based on heterogeneous graph meta-path views and network structure view for graph contrastive learning. This ...

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 ...

Accurate prediction of drug–target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for ...

2.3 GCATCMDA Figure 1 illustrates the workflow of GCATCMDA, a model based on graph neural networks and contrastive learning for predicting effective candidate sets of microbe-disease associations.

To overcome these limitations, researchers developed Graph Attention-aware Fusion Networks (GRAF), a framework designed to transform multiplex heterogeneous networks into unified, interpretable ...

2.1 Graph Reconstruction 2.2 Graph Contrastive Learning 2.3 Graph Representation Distillati 2.4 Adversarial Graph Learning 2.5 Score Prediction 3. Graph Anomaly Measures 3.1 One-class Classification ...

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 ...

Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks , Neural Information Processing Systems (NeurIPS) 2022. [2] Tiancheng Huang, Ke Xu, and Donglin Wang. Da-hgt: ...