Temporal Graph Representation Learning

Temporal graph representation learning (TGRL) aims to create low-dimensional representations of nodes in dynamic networks, capturing both structural and temporal information for improved analysis of evolving relationships. Current research focuses on developing efficient and scalable algorithms, often employing graph neural networks (GNNs) and transformers, to address challenges like high computational costs and the need for interpretability. These advancements are crucial for various applications, including link prediction, community detection, and anomaly detection in domains ranging from social networks to biological systems, enabling more accurate modeling and prediction in complex, time-varying data.