A Model for Urban Social Networks
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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 are inadequate in 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. The aim of our work is to fill the gap between these two approaches by defining a parametric data-driven model for urban social networks – implemented and released as open source software. Given a territory of interest, we construct an age-stratified and geo-referenced synthetic population whose individuals are connected by either kinship or friendship. The factors governing the structure of the obtained network, such as the dependency of friendship upon distance and age composition, are thoroughly studied through extensive simulations focused on three Italian cities of different size.
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