Graph Neural

Graph neural networks (GNNs) leverage the power of graph structures to model relationships within data, aiming to learn representations that capture complex dependencies and improve prediction accuracy across diverse applications. Current research focuses on enhancing GNN architectures, such as graph convolutional networks and graph attention networks, to address challenges like over-smoothing and uncertainty quantification, often incorporating techniques from ordinary and stochastic differential equations. These advancements are significantly impacting fields ranging from molecular dynamics and transportation planning to financial forecasting and brain signal analysis, enabling more accurate modeling and improved decision-making in complex systems.