Publications (new)
2023
Bokányi, E.; Heemskerk, E. M.; Takes, F. W.
The anatomy of a population-scale social network Journal Article
In: Scientific Reports, vol. 13, no. 1, pp. 9209, 2023, ISSN: 2045-2322, (Publisher: Nature Publishing Group).
Abstract | Links | BibTeX | Tags: Complex networks, Socioeconomic scenarios
@article{bokanyi_anatomy_2023,
title = {The anatomy of a population-scale social network},
author = {E. Bokányi and E. M. Heemskerk and F. W. Takes},
url = {https://www.nature.com/articles/s41598-023-36324-9},
doi = {10.1038/s41598-023-36324-9},
issn = {2045-2322},
year = {2023},
date = {2023-06-01},
urldate = {2024-04-08},
journal = {Scientific Reports},
volume = {13},
number = {1},
pages = {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.},
note = {Publisher: Nature Publishing Group},
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.