Dynamic Graph Neural Network

Dynamic Graph Neural Networks (DyGNNs) aim to learn representations from graphs whose structure and/or node/edge attributes change over time, addressing the limitations of static GNNs in modeling real-world evolving systems. Current research focuses on improving the expressive power of DyGNNs, often through novel architectures incorporating attention mechanisms, memory modules, and implicit neural networks, as well as developing efficient training frameworks and addressing challenges like scalability and robustness to distribution shifts. This field is significant because DyGNNs enable more accurate modeling of complex temporal processes across diverse applications, including anomaly detection, emotion recognition, and link prediction in dynamic networks.