Input Graph

Input graphs represent data as interconnected nodes and edges, enabling analysis of complex relationships in diverse domains. Current research focuses on improving graph neural network (GNN) performance by addressing issues like imbalanced data, over-smoothing, and computational cost, often through techniques such as data augmentation, prompt tuning, and graph sparsification. These advancements are driving progress in various applications, including node classification, graph generation, and action recognition, by enhancing model robustness, efficiency, and interpretability. The development of more powerful and efficient GNNs for handling large and complex input graphs remains a key area of ongoing investigation.