  Spreading or complex contagion processes on networks are an important
mechanistic foundation of tipping dynamics and other nonlinear phenomena in
complex social, ecological and technological systems. Increasing amounts of
temporal network data are now becoming available to study such spreading
processes of behaviours, opinions, ideas, diseases, innovations or technologies
and to test hypotheses regarding their specific properties. To this end, we
here present a methodology based on dose-response functions and hypothesis
testing using surrogate data sets. We demonstrate this methodology for
synthetic temporal network data generated by the adaptive voter model.
Furthermore, we apply it to empirical temporal network data from the Copenhagen
Networks Study. This data set provides a physically-close-contact network
between university students participating in the study over the course of three
months. We study the potential spreading dynamics of the health-related
behaviour "regularly going to the fitness studio" on this network. Based on a
hierarchy of surrogate data models, we find that the empirical data neither
provide significant evidence for an influence of a dose-response-type network
spreading process, nor significant evidence for homophily. The empirical
dynamics in exercise behaviour are likely better described by individual
features such as the disposition towards the behaviour, and the persistence to
maintain it, as well as external influences affecting the whole group, and the
non-trivial network structure. The proposed methodology is generic and
promising also for applications to other data sets and traits of interest.
