Spatial Network

Spatial networks analyze data structured by both relationships between entities (graph topology) and their geographic locations. Current research focuses on developing sophisticated models, such as graph neural networks and Bayesian neural networks, to capture the interplay between spatial geometry and network structure, often incorporating node attributes and temporal dynamics. These advancements improve prediction accuracy in diverse applications, including disease risk forecasting, autonomous driving, and optimizing sensor placement, by leveraging the combined power of spatial and relational information. The resulting insights are valuable across numerous fields, enabling more accurate modeling and improved decision-making in complex systems.