Supervised Graph Neural Network

Supervised graph neural networks (GNNs) leverage graph structures to improve the performance of supervised learning tasks by incorporating relational information between data points. Current research focuses on enhancing GNNs through self-supervised and semi-supervised learning techniques, often employing graph convolutional networks (GCNs) and contrastive learning methods to address challenges like data scarcity and noisy labels. These advancements are impacting diverse fields, improving accuracy in applications ranging from recommendation systems and protein-ligand binding affinity prediction to scene-based question answering and esports win prediction. The ability to effectively utilize both labeled and unlabeled data makes GNNs a powerful tool for various machine learning problems.