Complex systems can be modelled and analyzed using network analysis where components of the system are treated as nodes. In these networks, the nodes interact over time creating edges. The edges are random variables creating dynamic networks that change over time. Examples of dynamic networks include social networks, biological networks, financial networks, and computer networks. Typically, these networks are large and sparse: the number of nodes is large and the number of edges is small comparatively. In this session, the talks will discuss methodological advances stemming from the field of machine learning and statistics for modelling, monitoring, analyzing, and conducting inference within, large dynamic networks. 

Justification: Network analysis is an important and emerging field, where many challenges remain. One important challenge is the modelling, analysis, and monitoring of large dynamic networks. These methodologies have a wide application area from social networks, to finance and biology. At the same time, recent advances from machine learning proved their efficacy in modelling and predicting network dynamics. This session aims to showcase new advances in solving these important challenges and aims to give young researchers from across the world (The Netherlands, the USA, and Singapore) a platform to share their recent work in this area.