Time Temporal Graph

Time-temporal graphs represent dynamic relationships between entities that evolve over time, focusing on analyzing and predicting these changes. Current research emphasizes developing effective methods for embedding these graphs into lower-dimensional spaces, improving the accuracy and efficiency of tasks like link prediction and node classification, often employing techniques like Gaussian embeddings and transformer-based architectures. This field is crucial for understanding complex systems across various domains, from social networks and disease spread to recommendation systems and video analysis, enabling more accurate modeling and prediction of dynamic processes.