Graph Node Classification

Graph node classification aims to assign labels to nodes within a graph, leveraging both node features and the graph's structure. Current research emphasizes improving accuracy and efficiency, particularly in scenarios with limited labeled data, by employing techniques like graph neural networks (GNNs) with various architectural enhancements (e.g., incorporating global information, multi-view learning, and spectral methods) and advanced label propagation methods. These advancements are crucial for applications ranging from social network analysis and fraud detection to medical image analysis and improving the fairness and robustness of graph-based machine learning models.