Dynamic Network

Dynamic networks represent systems where relationships between entities change over time, demanding models that capture both structural and temporal dynamics. Current research focuses on developing efficient algorithms and architectures, such as graph neural networks, tensor factorization, and dynamic routing networks, to analyze these evolving structures and predict future states, often incorporating data from multiple modalities. This field is crucial for understanding complex systems across diverse domains, from social networks and communication systems to biological processes and autonomous driving, enabling improved prediction, anomaly detection, and resource optimization.