Beyond linearity and time-homogeneity: Relational hyper event models with time-varying non-linear effects

Technological advances enable the collection of many types of complex, dynamic, relational hyperevents. Despite the complexity of the data, most current Relational Hyper Event Models (RHEMs) use simple linear effects to describe the event rates. We extend the RHEM in order to allow non-linear effects that vary over time by using tensor product smooths. We validate our method on both synthetic and empirical data, examining evolving patterns and the impact of scientific collaboration between multiple players. Our approach provides new insights into hyperevent dynamics, uncovering potential non-monotonic patterns that linear models cannot capture. • Interpretation of linear RHEMs can dramatically fail in presence of non-monotonic effects. • No prior assumptions of linear time-homogeneous effects of drivers on event rate. • Inference in relational hyper event models framed as logistic additive regression. • Joint time-varying non-linear effects can be modeled as tensor product smooths. • Author self-citation, a scientific innovation driver, shows a non-monotonic effect.