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Defining accurate and flexible models for real-world networks of human beings is instrumental to understand the observed properties of phenomena taking place across those networks and to support computer simulations of dynamic processes of interest for several areas of research – including computational epidemiology, which is recently high on the agenda. In this paper we present a flexible model to generate age-stratified and geo-referenced synthetic social networks on the basis of widely available aggregated demographic data and, possibly, of estimated age-based social mixing patterns. Using the Italian city of Florence as a case study, we characterize our network model under selected configurations and we show its potential as a building block for the simulation of infections’ propagation. A fully operational and parametric implementation of our model is released as open-source.