Modeling framework unifying contact and social networks

Abstract

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.

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Cited by in corpus (6)

• The temporal dynamics of group interactions in higher-order social networks
• The structural evolution of temporal hypergraphs through the lens of hyper-cores
• Emerging Activity Temporal Hypergraph: a model for generating realistic time-varying hypergraphs
• Generating surrogate temporal networks from mesoscale building blocks
• Flow of temporal network properties under local aggregation and time shuffling: a tool for characterizing, comparing and classifying temporal networks
• Generalizing Egocentric Temporal Neighborhoods to probe for spatial correlations in temporal networks and infer their topology