Dynamic Graph

Dynamic graphs represent evolving relationships between entities, focusing on modeling changes in both network structure and node/edge attributes over time. Current research emphasizes developing efficient and expressive model architectures, such as dynamic graph neural networks (DyGNNs) and graph transformers, often incorporating techniques like graph coarsening, attention mechanisms, and temporal convolutional layers to capture complex spatiotemporal patterns. This field is significant for its applications in diverse areas including urban planning, social network analysis, and brain imaging, enabling improved prediction, anomaly detection, and a deeper understanding of complex systems. Furthermore, research is actively addressing challenges related to scalability, robustness to distribution shifts, and explainability of model predictions.