Dynamic networks occur in every field of science, technology, medicine, as well as everyday life. Understanding their behaviour has important applications. For example, whether it is to uncover serious crime on the dark web, intrusions in a computer network, or hijacks at global internet scales, better network anomaly detection tools are desperately needed in cyber-security. Characterising the network structure of multiple EEG time series recorded at different locations in the brain is critical for understanding neurological disorders and the development of therapeutics. Modelling dynamic networks is of great interest in transport applications, such as for incident mitigation on highways and predicting the influence of weather-related events on train networks. Systematically identifying, attributing, and preventing misinformation online requires realistic models of information flow in social networks. While the theory of simple random networks is well-established in mathematics and computer science, the recent explosion of dynamic network data has exposed a large gap in our ability to process real-world networks. Classical models do not capture the rich structure and temporal dynamics seen in real data, nor are they geared to answer practitioners’ typical questions, e.g. relating to exploratory data analysis, parameter estimation or prediction. The NeST programme will develop robust, principled, yet computationally feasible ways of modelling dynamically changing networks and the statistical processes on them.
