Prof. Renauld Lambiote, University of Oxford, UK (15:00-16:00)

Higher-Order Networks:

Network science provides powerful analytical and computational methods to describe the behaviour of complex systems. From a networks viewpoint, the system is seen as a collection of elements interacting through pairwise connections. Canonical examples include social networks, neuronal networks or the Web. Importantly, elements often interact directly with a relatively small number of other elements, while they may influence large parts of the system indirectly via chains of direct interactions. In other words, networks allow for a sparse architecture together with global connectivity. Compared with mean-field approaches, network models often have greater explanatory power because they account for the non-random topologies of real-life systems. However, new forms of high-dimensional and time-resolved data have now also shed light on the limitations of these models. In this talk, I will review recent advances in the development of higher-order network models, which account for different types of higher-order dependencies in complex data. Those include temporal networks, where the network is itself a dynamical entity and higher-order Markov models, where chains of interactions are more than a combination of links.