  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.
