Spatial Temporal Graph

Spatial-temporal graphs represent data with both spatial and temporal dependencies, aiming to model complex interactions and predict future states within dynamic systems. Current research heavily utilizes graph convolutional networks (GCNs) and transformers, often integrated with recurrent neural networks (RNNs), to capture these dependencies in various applications. These models are being refined through techniques like graph pruning, adaptive graph construction, and multi-scale approaches to improve efficiency and accuracy. The resulting advancements have significant implications for diverse fields, including traffic prediction, human activity recognition, and financial forecasting, by enabling more accurate and timely predictions from complex, evolving datasets.