GNN Prediction

Graph Neural Network (GNN) prediction focuses on leveraging the power of GNNs for various prediction tasks on graph-structured data, aiming to improve accuracy, robustness, and interpretability. Current research emphasizes higher-order GNNs to capture complex relationships within graphs, alongside methods for explaining GNN decisions through subgraph analysis and uncertainty quantification techniques like conformal prediction. These advancements are crucial for building reliable and trustworthy GNN models across diverse applications, from materials science (e.g., molecule generation) to cybersecurity (e.g., malicious domain detection), where understanding and mitigating prediction uncertainty is paramount.