Anomaly detection in dynamic networks

The temporal evolution of the structure of dynamic networks carries critical information about the development of complex systems in various applications, from biology to social networks. Deviations from regular network structure evolution may also provide critical information about anomalies or events of different forms. While these topics are of importance, the literature in network science, graph theory, or network machine learning, still lacks of relevant models for dynamic networks, proper metrics for comparing network structures, as well as scalable algorithms for anomaly detection. This project exactly aims at bridging these gaps.