The hybrid talk of our guest Leonie Neuhäuser from RWTH Aachen (Germany) will take place on Friday, July 22 at 3 pm CEST online and onsite in the Salon
If you would like to attend, please send us an email.
Abstract [Changed on July 19, 3:25h]:
Networks provide a powerful framework for the modelling of dynamical systems. However, it is increasingly realised that such pairwise interaction models may not be sufficient to describe a range of important phenomena which are determined by group interactions, ranging from social groups to neuronal activity interactions. In this work, we derive and analyse models for consensus dynamics on hypergraphs, where nodes interact in groups rather than in pairs. Our work reveals that multibody dynamical effects that go beyond rescaled pairwise interactions can appear only if the interaction function is nonlinear, regardless of the underlying multibody structure.
We thus focus on dynamics based on a certain class of non-linear interaction functions, which can model different sociological phenomena such as peer pressure and stubbornness. Unlike for linear consensus dynamics on networks, we show how our nonlinear model dynamics can cause shifts away from the average system state. Extending our work to the case of temporal group interactions, we find interaction effects between the polyadic and temporal dimension that result in a first-mover advantage in the consensus formation process: If there is a local majority opinion in the hyperedges that are active early on, then the majority in these first-mover groups has a higher influence on the final consensus value—a behaviour that is not observable in this form in projections of the temporal hypergraph. Our results show that when accounting for group interactions in networked systems, one has to take into account the type of dynamics present: there are no higher-order effects without non-linearity!
Leonie Neuhäuser is a PhD candidate in the Computational Network Sciences Group at RWTH Aachen University. Her research interests include Network Science, Complex Systems and Computational Social Sciences. In particular, Leonie focuses on the statistical and dynamical analysis of Social Networks with topics ranging from opinion dynamics on hypergraphs to the effects of systematic errors and bias on rankings in social networks.