Temporal Heterogeneous

Temporal heterogeneous networks (THNs) model evolving relationships between different types of entities over time, a crucial aspect of many real-world systems. Current research focuses on developing deep learning frameworks, such as graph neural networks (GNNs), that effectively capture both the heterogeneous nature of nodes and the temporal dynamics of their interactions, often employing techniques like attention mechanisms, Hawkes processes, and hyperbolic embeddings to improve representation learning. These advancements aim to enhance predictive capabilities in tasks like link prediction and anomaly detection within diverse applications, including recommendation systems, social networks, and industrial monitoring. The development of robust and scalable THN models is driving progress in understanding and predicting complex temporal dynamics in various domains.