Spatio Temporal Graph

Spatio-temporal graphs (STGs) represent data with both spatial and temporal dependencies, aiming to model and predict dynamic processes across interconnected entities. Current research focuses on developing sophisticated neural network architectures, such as graph convolutional networks (GCNs) combined with recurrent neural networks (RNNs) or transformers, to capture complex spatio-temporal interactions, often incorporating attention mechanisms and novel graph learning strategies to improve efficiency and accuracy. These models find applications in diverse fields, including traffic forecasting, urban mobility management, and even biological processes like gait analysis and apoptosis classification, offering improved prediction accuracy and enhanced understanding of dynamic systems. The development of explainable and robust STG models, addressing challenges like data scarcity and long-range dependencies, remains a key area of ongoing investigation.