Graph Ensemble

Graph ensembles combine multiple graph neural networks (GNNs) to improve the accuracy, robustness, and generalizability of predictions in various domains. Current research focuses on applying ensemble methods to enhance GNN performance in tasks such as material property prediction, medical image classification, and protein-protein interaction prediction, often employing techniques like prediction averaging and self-ensembling. This approach addresses limitations of individual GNNs, particularly in scenarios with limited data or complex relationships, leading to improved model performance and potentially impacting fields like materials science, healthcare, and education.