Abstract
Spreading dynamics and complex contagion processes on networks are important mechanisms underlying the emergence of critical transitions, tipping points and other non-linear phenomena in complex human and natural systems. Increasing amounts of temporal network data are now becoming available to study such spreading processes of behaviours, opinions, ideas, diseases and innovations 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 models that randomise most aspects of the empirical data while conserving certain structures relevant to contagion, group or homophily dynamics. We demonstrate this methodology for synthetic temporal network data of spreading processes 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 several hundreds of university students participating in the study over the course of 3 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 our method neither provides significant evidence for an influence of a dose–response-type network spreading process in this data set, 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 temporal network data sets and traits of interest.
Highlights
Spreading and complex contagion processes shape the dynamics of diverse complex ecological, societal and technological systems studied in many fields of research [1,2,3]
We investigate the empirical temporal network data obtained from the Copenhagen Networks Study (Sect. 4.2)
For K 30, the difference between the dose– response functions (DRFs) is obscured by the increasing errors in case A, but it is still clearly showing for the longer simulations in panel B
Summary
Spreading and complex contagion processes shape the dynamics of diverse complex ecological, societal and technological systems studied in many fields of research [1,2,3]. Nological innovations relevant for sustainability transition and rapid decarbonisation [15,16,17,18]; political changes [19]; or religious missionary work [20,21] These spreading processes on complex networks often give rise to non-linear dynamics and the emergence of macroscopic phenomena, such as phase transitions and tipping points that separate qualitatively different dynamical regimes [22]; for example, a transition between regimes where a local infection or innovation is locally contained, and those where it spreads globally to a large part of the network [1,2,10,23,24]. Spreading processes can interact with the underlying complex network structures, e.g. through the process of homophily, giving rise to complex coevolutionary feedbacks between dynamics on and structure of these
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