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Inferring Urban Social Networks from Publicly Available Data

dc.contributor.authorStefano Guarino
dc.contributor.authorEnrico Mastrostefano
dc.contributor.authorMassimo Bernaschi
dc.contributor.authorAlessandro Celestini
dc.contributor.authorMarco Cianfriglia
dc.contributor.authorDavide Torre
dc.contributor.authorLena Rebecca Zastrow
dc.contributor.otherIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
dc.contributor.otherIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
dc.contributor.otherIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
dc.contributor.otherIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
dc.contributor.otherIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
dc.contributor.otherIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
dc.contributor.otherIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
dc.date.accessioned2021-04-27T00:04:50Z
dc.date.available2025-10-02T03:44:10Z
dc.date.issued01-04-2021
dc.identifier.issn-
dc.identifier.urihttps://www.mdpi.com/1999-5903/13/5/108
dc.description.abstractThe 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 <i>contact</i> 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.
dc.format-
dc.language.isoEN
dc.publisherMDPI AG
dc.relation.uri['https://journals.oslomet.no/index.php/ar//about/submissions#authorGuidelines', 'http://www.artandresearch.info/', 'https://journals.oslomet.no/index.php/ar//about#focusAndScope']
dc.rightsCC BY
dc.subject['arts', 'music', 'visual arts', 'dance', 'drama', 'Arts in general', 'NX1-820']
dc.subject.lccInformation technology
dc.titleInferring Urban Social Networks from Publicly Available Data
dc.typeArticle
dc.description.keywordsurban social network
dc.description.keywordsgraph model
dc.description.keywordsdata-driven
dc.description.keywordsopen source
dc.description.keywordssimulator
dc.description.pages-
dc.description.doi10.3390/fi13050108
dc.title.journalFuture Internet
dc.identifier.e-issn1999-5903
dc.identifier.oaioai:doaj.org/journal:1b96055ce0cf41868a5cd91b9c15e371
dc.journal.infoVolume 13, Issue 5


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