Inferring Urban Social Networks from Publicly Available Data

Stefano Guarino,Davide Torre,Enrico Mastrostefano,Alessandro Celestini,Massimo Bernaschi,Lena Zastrow,Marco Cianfriglia

doi:10.3390/fi13050108

Stefano Guarino, Davide Torre + Show 5 more

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https://doi.org/10.3390/fi13050108

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Abstract

The definition of suitable generative models for synthetic yet realistic social networks is a widely studied problem 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. By using just widely available aggregated demographic and social-mixing data, we are able to create, for a territory of interest, 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.

Highlights

Introduction and BackgroundDefining accurate models for real-world social networks is instrumental in several research fields, e.g., in sociology [1], epidemiology [2], or marketing [3]
The structure of the network has a direct impact on the process, e.g., the topology of urban social networks, their size, and demography, can affect disease spreading [4] in and within cities
We present a novel computational model for urban social networks, that combines a data-driven framework with a set of tunable parameters

Summary

Introduction

Introduction and BackgroundDefining accurate models for real-world social networks is instrumental in several research fields, e.g., in sociology [1], epidemiology [2], or marketing [3]. Computer-based simulations of these models may represent a valuable tool to understand social phenomena, along with classic analytical studies. Dynamic processes, such as the spread of a disease or a rumor, can be represented upon suitable networks that encode the patterns of connection and interaction among the individuals of a population. The structure of the network has a direct impact on the process, e.g., the topology of urban social networks, their size, and demography, can affect disease spreading [4] in and within cities. The graph encodes information on the urban social fabric and, as such, it increases the plausibility of dynamic (e.g., transmission) processes that may be influenced by preferences and actions of agents and groups of related

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