GNN Performance

Graph Neural Network (GNN) performance research focuses on improving the efficiency and accuracy of GNNs, particularly when dealing with large-scale graphs and diverse data characteristics. Current efforts concentrate on optimizing training through techniques like graph reordering and exploring the relationship between graph topology (structure), node features, and model architecture choices, including the impact of homophily and heterophily. Understanding these relationships is crucial for selecting appropriate GNNs for specific tasks and datasets, ultimately enhancing the reliability and applicability of GNNs across various domains, from social network analysis to medical image processing.