Spatial Graph

Spatial graphs represent data with both spatial and relational information, aiming to model complex systems where entities interact based on their location and connections. Current research focuses on developing and applying graph neural networks (GNNs), including spatio-spectral and adaptive variations, to analyze and predict phenomena within these graphs, addressing challenges like irregular domains and high computational costs. These methods find applications in diverse fields, from solving partial differential equations and improving large language model reasoning to enhancing autonomous navigation and predicting disease spread, demonstrating the broad utility of spatial graph analysis. The ability to effectively model spatial relationships within complex systems is driving significant advancements across numerous scientific disciplines and practical applications.