Semi Supervised Node Classification

Semi-supervised node classification aims to predict the labels of nodes in a graph using a limited number of labeled nodes and a wealth of unlabeled ones. Current research focuses on improving Graph Neural Networks (GNNs), including variations like Graph Transformers and hypergraph neural networks, by addressing challenges such as over-smoothing, heterophily (nodes with dissimilar labels being connected), and the impact of graph noise. These advancements aim to enhance accuracy, efficiency, and robustness, particularly in handling large graphs and diverse data distributions. The field's impact spans various applications, including social network analysis, recommendation systems, and knowledge graph reasoning.