Dose-response functions and surrogate models for exploring social contagion in the Copenhagen Networks Study

TL;DR

This paper introduces a methodology using dose-response functions and surrogate data models to analyze contagion processes in temporal networks, applied to synthetic and real-world data from the Copenhagen Networks Study, focusing on health behavior spread.

Contribution

The paper presents a novel approach combining dose-response functions with surrogate data testing to analyze contagion dynamics in temporal networks, validated on empirical and synthetic data.

Findings

No significant evidence for contagion-driven spread of exercise behavior in the data.

Empirical dynamics are better explained by individual traits and external influences.

The methodology is versatile and applicable to various temporal network datasets.

Abstract

Spreading dynamics and complex contagion processes on networks are important mechanisms underlying the emergence of critical transitions, tipping points and other nonlinear 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 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 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.