Node Classification Datasets

Node classification datasets are crucial for training and evaluating graph neural networks (GNNs), which aim to learn patterns from graph-structured data by classifying individual nodes. Current research focuses on improving GNN performance in scenarios with limited labeled data (few-shot learning) and scaling GNN training to handle massive graphs, employing techniques like curriculum learning, graph coarsening, and attention mechanisms that leverage node homophily. These advancements are significant because they enhance the applicability of GNNs to real-world problems where labeled data is scarce and graph sizes are enormous, impacting fields like social network analysis, recommendation systems, and drug discovery.