Inferring urban social networks from publicly available data

TL;DR

This paper introduces a data-driven, open-source model for creating realistic urban social networks using demographic and social-mixing data, capturing both household and friendship ties for applications like epidemiology.

Contribution

It presents a novel probabilistic model for friendship formation based on distance and age, filling the gap between random graph models and contact networks.

Findings

The model accurately reproduces demographic and geographic features of urban social networks.

Simulations on Italian cities demonstrate the model's flexibility and realism.

The approach is applicable for epidemiological and urban planning studies.

Abstract

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.