Modeling framework unifying contact and social networks

Autor: Didier Le Bail, Mathieu Génois, Alain Barrat
Přispěvatelé: Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), CPT - E5 Physique statistique et systèmes complexes, Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), ANR-19-CE46-0008,DataRedux,Réduction de données massives pour la simulation numérique prédictive(2019)
Rok vydání: 2023
Předmět:
Zdroj: Physical Review E : Statistical, Nonlinear, and Soft Matter Physics
Physical Review E : Statistical, Nonlinear, and Soft Matter Physics, 2023, 107, pp.024301. ⟨10.1103/PhysRevE.107.024301⟩
ISSN: 2470-0053
2470-0045
1539-3755
1550-2376
DOI: 10.1103/physreve.107.024301
Popis: International audience; Temporal networks of face-to-face interactions between individuals are useful proxies of the dynamics of social systems on fast time scales. Several empirical statistical properties of these networks have been shown to be robust across a large variety of contexts. In order to better grasp the role of various mechanisms of social interactions in the emergence of these properties, models in which schematic implementations of such mechanisms can be carried out have proven useful. Here, we put forward a new framework to model temporal networks of human interactions, based on the idea of a co-evolution and feedback between (i) an observed network of instantaneous interactions and (ii) an underlying unobserved social bond network: social bonds partially drive interaction opportunities, and in turn are reinforced by interactions and weakened or even removed by the lack of interactions. Through this co-evolution, we also integrate in the model well-known mechanisms such as triadic closure, but also the impact of shared social context and non-intentional (casual) interactions, with several tunable parameters. We then propose a method to compare the statistical properties of each version of the model with empirical face-to-face interaction data sets, to determine which sets of mechanisms lead to realistic social temporal networks within this modeling framework.
Databáze: OpenAIRE
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