2023
Bokányi, Eszter; Heemskerk, Eelke M.; Takes, Frank W.
The anatomy of a population-scale social network Journal Article
In: Sci Rep, vol. 13, iss. 9209, 2023.
Abstract | Links | BibTeX | Tags: complex networks, socioeconomic scenarios
@article{Bokányi2023,
title = {The anatomy of a population-scale social network},
author = {Eszter Bokányi and Eelke M. Heemskerk and Frank W. Takes },
url = {https://www.nature.com/articles/s41598-023-36324-9#citeas},
doi = {https://doi.org/10.1038/s41598-023-36324-9},
year = {2023},
date = {2023-06-06},
urldate = {2023-06-06},
journal = {Sci Rep},
volume = {13},
issue = {9209},
abstract = {Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level. Our work provides new entry points to understand individual socio-economic failure and success as well as persistent societal problems of inequality and segregation.},
keywords = {complex networks, socioeconomic scenarios},
pubstate = {published},
tppubtype = {article}
}
Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level. Our work provides new entry points to understand individual socio-economic failure and success as well as persistent societal problems of inequality and segregation.