Temporal Graph Network

Temporal graph networks (TGNs) are a class of machine learning models designed to analyze data represented as graphs that evolve over time, aiming to learn meaningful representations from these dynamic interactions. Current research focuses on improving the efficiency and scalability of TGNs, often leveraging architectures inspired by transformers and exploring adaptive neighborhood sampling techniques to enhance performance. This field is significant due to its broad applicability across diverse domains, including financial fraud detection, recommendation systems, and human motion prediction, where understanding temporal dynamics within networked data is crucial for accurate modeling and prediction.