GNN Cl

Graph Neural Networks (GNNs) are increasingly used to analyze graph-structured data, addressing diverse objectives like node classification, clustering, and property prediction across various domains. Current research focuses on improving GNN robustness and fairness, particularly by addressing issues like over-smoothing, distributional shifts, and biases in data. This involves developing novel architectures, such as those incorporating conformal prediction for uncertainty quantification, and advanced sampling techniques to handle large graphs efficiently. The resulting advancements have significant implications for fields ranging from drug discovery and materials science to financial fraud detection and computer vision.