2023
de Jong, Rachel G.; van der Loo, Mark P. J.; Takes, Frank W.
Beyond the ego network: The effect of distant connections on node anonymity Working paper Forthcoming
Forthcoming.
Abstract | Links | BibTeX | Tags:
@workingpaper{nokey,
title = {Beyond the ego network: The effect of distant connections on node anonymity},
author = {Rachel G. de Jong and Mark P.J. van der Loo and Frank W. Takes},
url = {https://arxiv.org/abs/2306.13508},
year = {2023},
date = {2023-06-23},
abstract = {Ensuring privacy of individuals is of paramount importance to social network analysis research. Previous work assessed anonymity in a network based on the non-uniqueness of a node's ego network. In this work, we show that this approach does not adequately account for the strong de-anonymizing effect of distant connections. We first propose the use of d-k-anonymity, a novel measure that takes knowledge up to distance d of a considered node into account. Second, we introduce anonymity-cascade, which exploits the so-called infectiousness of uniqueness: mere information about being connected to another unique node can make a given node uniquely identifiable. These two approaches, together with relevant "twin node" processing steps in the underlying graph structure, offer practitioners flexible solutions, tunable in precision and computation time. This enables the assessment of anonymity in large-scale networks with up to millions of nodes and edges. Experiments on graph models and a wide range of real-world networks show drastic decreases in anonymity when connections at distance 2 are considered. Moreover, extending the knowledge beyond the ego network with just one extra link often already decreases overall anonymity by over 50%. These findings have important implications for privacy-aware sharing of sensitive network data.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workingpaper}
}
Ensuring privacy of individuals is of paramount importance to social network analysis research. Previous work assessed anonymity in a network based on the non-uniqueness of a node's ego network. In this work, we show that this approach does not adequately account for the strong de-anonymizing effect of distant connections. We first propose the use of d-k-anonymity, a novel measure that takes knowledge up to distance d of a considered node into account. Second, we introduce anonymity-cascade, which exploits the so-called infectiousness of uniqueness: mere information about being connected to another unique node can make a given node uniquely identifiable. These two approaches, together with relevant "twin node" processing steps in the underlying graph structure, offer practitioners flexible solutions, tunable in precision and computation time. This enables the assessment of anonymity in large-scale networks with up to millions of nodes and edges. Experiments on graph models and a wide range of real-world networks show drastic decreases in anonymity when connections at distance 2 are considered. Moreover, extending the knowledge beyond the ego network with just one extra link often already decreases overall anonymity by over 50%. These findings have important implications for privacy-aware sharing of sensitive network data.
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.
Kazmina, Yuliia; Heemskerk, Eelke M.; Bokanyi, Eszter; Takes, Frank W.
Socio-economic Segregation in a Population-Scale Social Network Working paper Forthcoming
Forthcoming.
Abstract | Links | BibTeX | Tags:
@workingpaper{nokey,
title = {Socio-economic Segregation in a Population-Scale Social Network},
author = {Yuliia Kazmina and Eelke M. Heemskerk and Eszter Bokanyi and Frank W. Takes},
url = {https://arxiv.org/abs/2305.02062},
year = {2023},
date = {2023-00-00},
urldate = {2023-00-00},
abstract = {We propose a social network-aware approach to studying socio-economic segregation. The key question that we address is whether patterns of segregation are more pronounced in social networks than the common spatial neighborhood-focused manifestations of segregation. We, therefore, conduct a population-scale social network analysis to study socio-economic segregation at a comprehensive and highly granular social network level: 17.2 million registered residents of the Netherlands that are connected through around 1.3 billion ties distributed over four distinct tie types. We take income assortativity as a measure of socio-economic segregation, compare a social network and spatial neighborhood approach, and find that the social network structure exhibits two times as much segregation. As such, this work challenges the dominance of the spatial perspective on segregation in both literature and policymaking. While at a particular scale of spatial aggregation (e.g., the geographical neighborhood), patterns of socio-economic segregation may appear relatively minimal, they may in fact persist in the underlying social network structure. Furthermore, we discover higher socio-economic segregation in larger cities, shedding a different light on the common view of cities as hubs for diverse socio-economic mixing. A population-scale social network perspective hence offers a way to uncover hitherto 'hidden' segregation that extends beyond spatial neighborhoods and infiltrates multiple aspects of human life.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workingpaper}
}
We propose a social network-aware approach to studying socio-economic segregation. The key question that we address is whether patterns of segregation are more pronounced in social networks than the common spatial neighborhood-focused manifestations of segregation. We, therefore, conduct a population-scale social network analysis to study socio-economic segregation at a comprehensive and highly granular social network level: 17.2 million registered residents of the Netherlands that are connected through around 1.3 billion ties distributed over four distinct tie types. We take income assortativity as a measure of socio-economic segregation, compare a social network and spatial neighborhood approach, and find that the social network structure exhibits two times as much segregation. As such, this work challenges the dominance of the spatial perspective on segregation in both literature and policymaking. While at a particular scale of spatial aggregation (e.g., the geographical neighborhood), patterns of socio-economic segregation may appear relatively minimal, they may in fact persist in the underlying social network structure. Furthermore, we discover higher socio-economic segregation in larger cities, shedding a different light on the common view of cities as hubs for diverse socio-economic mixing. A population-scale social network perspective hence offers a way to uncover hitherto 'hidden' segregation that extends beyond spatial neighborhoods and infiltrates multiple aspects of human life.
de Jong, Rachel G.; van der Loo, Mark P. J.; Takes, Frank W.
Algorithms for Efficiently Computing Structural Anonymity in Complex Networks Working paper Forthcoming
Forthcoming.
BibTeX | Tags:
@workingpaper{nokey,
title = {Algorithms for Efficiently Computing Structural Anonymity in Complex Networks},
author = {Rachel G. de Jong and Mark P.J. van der Loo and Frank W. Takes },
year = {2023},
date = {2023-00-00},
journal = {ACM Journal of Experimental Algorithmics},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workingpaper}
}
2022
de Zoete, Bart
Measuring Social Capital in a Population-scale Social Network Masters Thesis
2022.
Abstract | Links | BibTeX | Tags:
@mastersthesis{nokey,
title = {Measuring Social Capital in a Population-scale Social Network},
author = {Bart de Zoete },
url = {https://theses.liacs.nl/2319},
year = {2022},
date = {2022-09-01},
urldate = {2023-06-00},
abstract = {Are social connections primarily sources for opportunity and development or rather the building blocks for social segregation? Social capital studies have provided fragmented evidence of what the important indicators are, reflecting the theoretical diversity in understanding social capital and the subsequent wide range of survey-based studies and diverse conceptual operationalizations. We suggest a novel approach where we use precise social network measure of an individual’s social capital based on highly complete country register-derived population-scale social network data of 17.2 million people in the Netherlands. We investigate the combined effect of two simple and straightforward measures that capture the theoretically established concepts of bonding and bridging social capital.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Are social connections primarily sources for opportunity and development or rather the building blocks for social segregation? Social capital studies have provided fragmented evidence of what the important indicators are, reflecting the theoretical diversity in understanding social capital and the subsequent wide range of survey-based studies and diverse conceptual operationalizations. We suggest a novel approach where we use precise social network measure of an individual’s social capital based on highly complete country register-derived population-scale social network data of 17.2 million people in the Netherlands. We investigate the combined effect of two simple and straightforward measures that capture the theoretically established concepts of bonding and bridging social capital.
de Jong, Rachel G.
Measuring Structural Anonymity in Complex Networks Masters Thesis
2022.
Abstract | Links | BibTeX | Tags:
@mastersthesis{nokey,
title = {Measuring Structural Anonymity in Complex Networks },
author = {Rachel G. de Jong},
url = {https://theses.liacs.nl/2056},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
abstract = {In this thesis, we measure anonymity in networks by using the notion of d-k-anonymity. Using this measure, nodes are equivalent if their d-neighborhoods are isomorphic, and they have the same structural position in their d-neighborhood. We test the measure on graph models with increasing densities (Erdős-Rényi, Barabási-Albert models and the powerlaw cluster graph) and the family layer (parent-child relations, both an undirected and undirected version) of the Population-scale Social Network studied in the POPNET project. We additionally listed the most rare and most common neighborhoods in this layer of the network.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In this thesis, we measure anonymity in networks by using the notion of d-k-anonymity. Using this measure, nodes are equivalent if their d-neighborhoods are isomorphic, and they have the same structural position in their d-neighborhood. We test the measure on graph models with increasing densities (Erdős-Rényi, Barabási-Albert models and the powerlaw cluster graph) and the family layer (parent-child relations, both an undirected and undirected version) of the Population-scale Social Network studied in the POPNET project. We additionally listed the most rare and most common neighborhoods in this layer of the network.