Temporal Graph

Temporal graphs represent dynamic relationships between entities evolving over time, aiming to model and predict these changes. Current research focuses on developing sophisticated neural network architectures, such as graph convolutional networks (GCNs) and transformers, often incorporating mechanisms like attention and recurrent layers to capture complex spatio-temporal dependencies. These advancements are improving the accuracy of predictions in diverse applications, including traffic forecasting, disease outbreak prediction, and even medical diagnosis using electronic health records. The field is also actively exploring efficient training methods and developing standardized benchmarks for evaluating model performance across various datasets and tasks.