Continuous Time Dynamic Graph

Continuous-time dynamic graphs (CTDGs) model evolving relationships in data where interactions occur at irregular time intervals, aiming to learn representations that capture both structural and temporal patterns. Current research focuses on developing efficient and accurate algorithms, including graph neural networks (GNNs) and state-space models, that address challenges like long-term dependency modeling, handling noisy data, and achieving low-latency inference. These advancements are crucial for various applications, such as anomaly detection, link prediction, and risk management in domains ranging from social networks to financial transactions, improving the accuracy and efficiency of analyses on complex, evolving systems. A key area of ongoing work is developing standardized benchmark frameworks for more robust model evaluation and comparison.