Shot Node Classification

Few-shot node classification tackles the challenge of classifying nodes in graphs with limited labeled data per class, aiming to improve the accuracy and efficiency of graph-based machine learning. Current research focuses on enhancing graph neural networks (GNNs) through techniques like meta-learning, contrastive learning, and self-training, often incorporating large language models or employing curriculum learning strategies to improve generalization to unseen classes. These advancements are significant because they enable effective analysis of real-world graphs where labeled data is scarce, impacting diverse applications from social network analysis to drug discovery.