The basic interaction unit of many dynamical systems involves more than two nodes. In such situations where networks are not an appropriate modelling framework, it has recently become increasingly popular to turn to higher-order models. In this collection of papers, we derive and analyse models for consensus dynamics on hypergraphs, where nodes interact in groups rather than in pairs.
Most network analyses implicitly assume that the considered data is error-free and reliable. Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community. We model how such systematic uncertainty on edges of an attributed network can impact network analysis.