Temporal Graph Embedding is a technique that aims to represent nodes and edges in a dynamic graph within a low-dimensional vector space, capturing their temporal evolution. By learning meaningful representations, these embeddings enable various downstream tasks such as link prediction, anomaly detection, and community discovery in time-varying networks. These models effectively capture the evolving relationships between entities over time, providing valuable insights into dynamic systems like social networks, citation networks, and financial transactions.
