Inferring Urban Social Networks from Publicly Available Data
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/13/5/108 |
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| author | Stefano Guarino Enrico Mastrostefano Massimo Bernaschi Alessandro Celestini Marco Cianfriglia Davide Torre Lena Rebecca Zastrow |
| author_facet | Stefano Guarino Enrico Mastrostefano Massimo Bernaschi Alessandro Celestini Marco Cianfriglia Davide Torre Lena Rebecca Zastrow |
| author_sort | Stefano Guarino |
| collection | DOAJ |
| description | 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 <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. |
| first_indexed | 2024-03-10T11:56:41Z |
| format | Article |
| id | doaj.art-1b96055ce0cf41868a5cd91b9c15e371 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T11:56:41Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-1b96055ce0cf41868a5cd91b9c15e3712023-11-21T17:15:54ZengMDPI AGFuture Internet1999-59032021-04-0113510810.3390/fi13050108Inferring Urban Social Networks from Publicly Available DataStefano Guarino0Enrico Mastrostefano1Massimo Bernaschi2Alessandro Celestini3Marco Cianfriglia4Davide Torre5Lena Rebecca Zastrow6Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, ItalyIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, ItalyIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, ItalyIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, ItalyIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, ItalyIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, ItalyIstituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, 00185 Rome, ItalyThe 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.https://www.mdpi.com/1999-5903/13/5/108urban social networkgraph modeldata-drivenopen sourcesimulator |
| spellingShingle | Stefano Guarino Enrico Mastrostefano Massimo Bernaschi Alessandro Celestini Marco Cianfriglia Davide Torre Lena Rebecca Zastrow Inferring Urban Social Networks from Publicly Available Data Future Internet urban social network graph model data-driven open source simulator |
| title | Inferring Urban Social Networks from Publicly Available Data |
| title_full | Inferring Urban Social Networks from Publicly Available Data |
| title_fullStr | Inferring Urban Social Networks from Publicly Available Data |
| title_full_unstemmed | Inferring Urban Social Networks from Publicly Available Data |
| title_short | Inferring Urban Social Networks from Publicly Available Data |
| title_sort | inferring urban social networks from publicly available data |
| topic | urban social network graph model data-driven open source simulator |
| url | https://www.mdpi.com/1999-5903/13/5/108 |
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