Temporal Network

Temporal networks model dynamic relationships between entities, evolving over time, with primary objectives of understanding and predicting these changes. Current research focuses on developing sophisticated algorithms and model architectures, including graph neural networks, recurrent neural networks, and contrastive learning methods, to analyze these networks and perform tasks like link prediction and community detection. This field is significant for its broad applicability across diverse domains, from social network analysis and disease spread modeling to traffic flow prediction and financial market analysis, offering powerful tools for understanding complex systems.