Laurent Hébert-Dufresne
Talk recording
Mathematical modeling of complex networks starts with a description of network data in terms of key structural features of the described network. This description constrained ensemble of random networks, whose constraints range in complexity from using the entire network structure (message passing approaches) to only using its degree distribution (configuration model) or density (Erdős-Rényi graphs). However, none of the resulting analytical descriptions are exact on complex networks and it is rarely clear when a more complicated model is justified by the data or by the quality of predictions. We will review new advances in network analyses and network descriptions to draw parallels between network modelling and network compression. In doing so, we outline a possible agenda for the future of mathematical network models.



