Class Node

Class node research focuses on improving the performance and efficiency of graph neural networks (GNNs) for node classification tasks, particularly addressing challenges posed by large graphs and imbalanced datasets. Current research emphasizes developing novel algorithms and architectures, such as training-free graph condensation methods and mixup-based techniques, to enhance GNNs' ability to learn effective node representations even with limited labeled data or the presence of unseen classes. These advancements are significant because they improve the accuracy and scalability of GNNs for various applications, including social network analysis, recommendation systems, and knowledge graph reasoning.