The emergence of social networks and the definition of suitable generative
models for synthetic yet realistic social graphs are widely studied problems in
the literature. By not being tied to any real data, random graph models cannot
capture all the subtleties of real networks and are inadequate for many
practical contexts -- including areas of research, such as computational
epidemiology, which are recently high on the agenda. At the same time, the
so-called contact networks describe interactions, rather than relationships,
and are strongly dependent on the application and on the size and quality of
the sample data used to infer them. To fill the gap between these two
approaches, we present a data-driven model for urban social networks,
implemented and released as open source software. Given a territory of
interest, and only based on widely available aggregated demographic and
social-mixing data, we construct an age-stratified and geo-referenced synthetic
population whose individuals are connected by "strong ties" of two types:
intra-household (e.g., kinship) or friendship. While household links are
entirely data-driven, we propose a parametric probabilistic model for
friendship, based on the assumption that distances and age differences play a
role, and that not all individuals are equally sociable. The demographic and
geographic factors governing the structure of the obtained network, under
different configurations, are thoroughly studied through extensive simulations
focused on three Italian cities of different size