2024
Kazmina, Yuliia; Heemskerk, Eelke M.; Bokányi, Eszter; Takes, Frank W.
From Contact to Threat: A Social Network Perspective on Perceptions of Immigration Journal Article Forthcoming
In: Forthcoming.
@article{nokeyh,
title = {From Contact to Threat: A Social Network Perspective on Perceptions of Immigration},
author = {Yuliia Kazmina and Eelke M. Heemskerk and Eszter Bokányi and Frank W. Takes
},
year = {2024},
date = {2024-07-09},
abstract = {Our perceptions are shaped by the social networks we are embedded in. Despite the acknowledged influence of close contacts on how we perceive the world, the role of the broader social environment remains opaque. Here, we leverage a unique combination of population-scale social network and survey data on perceptions of immigration. We find that both direct contacts and a wider social network exposure to migrants matter. Notably, for natives, network exposure shows a shift from positive to negative association with perceptions of immigration beyond a certain exposure threshold. The multi-layer nature of our data highlights this tipping point for next-door neighbours, with private social contexts exhibiting a positive relationship between exposure and immigration perceptions. Furthermore, it shows that contacts spanning multiple contexts also strengthen this relationship. The provided insights on the interplay between network composition and attitudes toward immigration highlight generic patterns shaping public opinion on pressing societal issues.
},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
Our perceptions are shaped by the social networks we are embedded in. Despite the acknowledged influence of close contacts on how we perceive the world, the role of the broader social environment remains opaque. Here, we leverage a unique combination of population-scale social network and survey data on perceptions of immigration. We find that both direct contacts and a wider social network exposure to migrants matter. Notably, for natives, network exposure shows a shift from positive to negative association with perceptions of immigration beyond a certain exposure threshold. The multi-layer nature of our data highlights this tipping point for next-door neighbours, with private social contexts exhibiting a positive relationship between exposure and immigration perceptions. Furthermore, it shows that contacts spanning multiple contexts also strengthen this relationship. The provided insights on the interplay between network composition and attitudes toward immigration highlight generic patterns shaping public opinion on pressing societal issues.
de Jong, Rachel G.; van der Loo, Mark P. J.; Takes, Frank W.
A systematic comparison of measures for k-anonymity in networks Journal Article
In: 2024.
Abstract | Links | BibTeX | Tags:
@article{nokeyg,
title = {A systematic comparison of measures for k-anonymity in networks},
author = {Rachel G. de Jong and Mark P. J. van der Loo and Frank W. Takes},
url = {https://doi.org/10.48550/arXiv.2407.02290
},
year = {2024},
date = {2024-07-02},
urldate = {2024-07-02},
abstract = {Privacy-aware sharing of network data is a difficult task due to the interconnectedness of individuals in networks. An important part of this problem is the inherently difficult question of how in a particular situation the privacy of an individual node should be measured. To that end, in this paper we propose a set of aspects that one should consider when choosing a measure for privacy. These aspects include the type of desired privacy and attacker scenario against which the measure protects, utility of the data, the type of desired output, and the computational complexity of the chosen measure. Based on these aspects, we provide a systematic overview of existing approaches in the literature. We then focus on a set of measures that ultimately enables our objective: sharing the anonymized full network dataset with limited disclosure risk. The considered measures, each based on the concept of k-anonymity, account for the structure of the surroundings of a certain node and differ in completeness and reach of the structural information taken into account. We present a comprehensive theoretical characterization as well as comparative empirical experiments on a wide range of real-world network datasets with up to millions of edges. We find that the choice of the measure has an enormous effect on aforementioned aspects. Most interestingly, we find that the most effective measures consider a greater node vicinity, yet utilize minimal structural information and thus use minimal computational resources. This finding has important implications for researchers and practitioners, who may, based on the recommendations given in this paper, make an informed choice on how to safely share large-scale network data in a privacy-aware manner.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Privacy-aware sharing of network data is a difficult task due to the interconnectedness of individuals in networks. An important part of this problem is the inherently difficult question of how in a particular situation the privacy of an individual node should be measured. To that end, in this paper we propose a set of aspects that one should consider when choosing a measure for privacy. These aspects include the type of desired privacy and attacker scenario against which the measure protects, utility of the data, the type of desired output, and the computational complexity of the chosen measure. Based on these aspects, we provide a systematic overview of existing approaches in the literature. We then focus on a set of measures that ultimately enables our objective: sharing the anonymized full network dataset with limited disclosure risk. The considered measures, each based on the concept of k-anonymity, account for the structure of the surroundings of a certain node and differ in completeness and reach of the structural information taken into account. We present a comprehensive theoretical characterization as well as comparative empirical experiments on a wide range of real-world network datasets with up to millions of edges. We find that the choice of the measure has an enormous effect on aforementioned aspects. Most interestingly, we find that the most effective measures consider a greater node vicinity, yet utilize minimal structural information and thus use minimal computational resources. This finding has important implications for researchers and practitioners, who may, based on the recommendations given in this paper, make an informed choice on how to safely share large-scale network data in a privacy-aware manner.
Menyhért, Márton; Bokányi, Eszter; Corten, Rense; Heemskerk, Eelke M.; Kazmina, Yuliia; Takes, Frank W.
Connectivity and Community Structure of Online and Register-based Social Networks Journal Article
In: 2024.
Abstract | Links | BibTeX | Tags:
@article{nokeyf,
title = {Connectivity and Community Structure of Online and Register-based Social Networks},
author = {Márton Menyhért and Eszter Bokányi and Rense Corten and Eelke M. Heemskerk and Yuliia Kazmina and Frank W. Takes},
url = {https://doi.org/10.48550/arXiv.2406.17752
},
year = {2024},
date = {2024-06-25},
urldate = {2024-06-25},
abstract = {The dominance of online social media data as a source of population-scale social network studies has recently been challenged by networks constructed from government-curated register data. In this paper, we investigate how the two compare, focusing on aggregations of the Dutch online social network (OSN) Hyves and a register-based social network (RSN) of the Netherlands. First and foremost, we find that the connectivity of the two population-scale networks is strikingly similar, especially between closeby municipalities, with more long-distance ties captured by the OSN. This result holds when correcting for population density and geographical distance, notwithstanding that these two patterns appear to be the main drivers of connectivity. Second, we show that the community structure of neither network follows strict administrative geographical delineations (e.g., provinces). Instead, communities appear to either center around large metropolitan areas or, outside of the country's most urbanized area, are comprised of large blocks of interdependent municipalities. Interestingly, beyond population and distance-related patterns, communities also highlight the persistence of deeply rooted historical and sociocultural communities based on religion. The results of this study suggest that both online social networks and register-based social networks are valuable resources for insights into the social network structure of an entire population.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The dominance of online social media data as a source of population-scale social network studies has recently been challenged by networks constructed from government-curated register data. In this paper, we investigate how the two compare, focusing on aggregations of the Dutch online social network (OSN) Hyves and a register-based social network (RSN) of the Netherlands. First and foremost, we find that the connectivity of the two population-scale networks is strikingly similar, especially between closeby municipalities, with more long-distance ties captured by the OSN. This result holds when correcting for population density and geographical distance, notwithstanding that these two patterns appear to be the main drivers of connectivity. Second, we show that the community structure of neither network follows strict administrative geographical delineations (e.g., provinces). Instead, communities appear to either center around large metropolitan areas or, outside of the country's most urbanized area, are comprised of large blocks of interdependent municipalities. Interestingly, beyond population and distance-related patterns, communities also highlight the persistence of deeply rooted historical and sociocultural communities based on religion. The results of this study suggest that both online social networks and register-based social networks are valuable resources for insights into the social network structure of an entire population.
de Bel, Vera; Bokányi, Eszter; Karsten, Hank; Leopold, Thomas
In: 2024.
Abstract | Links | BibTeX | Tags:
@article{nokeye,
title = {A parallel kinship universe? Using Dutch kinship network data to replicate Kolk et al.’s (2023) demographic account of kinship networks in Sweden},
author = {Vera de Bel and Eszter Bokányi and Hank Karsten and Thomas Leopold},
url = {https://osf.io/preprints/socarxiv/3k6nq},
year = {2024},
date = {2024-06-11},
urldate = {2024-06-11},
abstract = {This commentary on “The Swedish Kinship Universe” by Kolk, Andersson, Pettersson, & Drefahl (2023) examines whether the Swedish findings are generalizable to another demographically advanced population, the Netherlands, and whether differences in cohort fertility patterns and divorce rates affect the frequencies of different kin types. Our commentary also contributes a further empirical validation of kinship statistics calculated from microsimulations and aggregate fertility and mortality data. By analyzing Dutch kinship network data consisting of ties to grandchildren, children, nieces, nephews, siblings, cousins, parents, aunts, uncles, and grandparents, we first identified large similarities, corroborating that the picture drawn by Kolk et al. (2023) might indeed be generalized to other demographically advanced (Western) contexts. Second, we observed a trickling down of differences from one generation to the next one (reflected in higher numbers of aunts, uncles, and cousins in the post-SDT cohort resulting from the Dutch baby boom). This finding demonstrates how periodic differences such as the intensity of the baby boom produce a lasting legacy in the demography of families, even if these differences were relatively short-lived. Third, other family-related behavioral changes – specifically separation and divorce – play an important role in shaping kinship networks and cross-national differences in their composition.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This commentary on “The Swedish Kinship Universe” by Kolk, Andersson, Pettersson, & Drefahl (2023) examines whether the Swedish findings are generalizable to another demographically advanced population, the Netherlands, and whether differences in cohort fertility patterns and divorce rates affect the frequencies of different kin types. Our commentary also contributes a further empirical validation of kinship statistics calculated from microsimulations and aggregate fertility and mortality data. By analyzing Dutch kinship network data consisting of ties to grandchildren, children, nieces, nephews, siblings, cousins, parents, aunts, uncles, and grandparents, we first identified large similarities, corroborating that the picture drawn by Kolk et al. (2023) might indeed be generalized to other demographically advanced (Western) contexts. Second, we observed a trickling down of differences from one generation to the next one (reflected in higher numbers of aunts, uncles, and cousins in the post-SDT cohort resulting from the Dutch baby boom). This finding demonstrates how periodic differences such as the intensity of the baby boom produce a lasting legacy in the demography of families, even if these differences were relatively short-lived. Third, other family-related behavioral changes – specifically separation and divorce – play an important role in shaping kinship networks and cross-national differences in their composition.
Kazmina, Yuliia; Heemskerk, Eelke M.; Bokanyi, Eszter; Takes, Frank W.
Socio-economic Segregation in a Population-Scale Social Network Journal Article
In: Social Networks, vol. 78, pp. 279–291, 2024.
Abstract | Links | BibTeX | Tags:
@article{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 = {2024},
date = {2024-04-21},
urldate = {2024-04-21},
journal = {Social Networks},
volume = {78},
pages = {279–291},
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 = {published},
tppubtype = {article}
}
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.
Beyond the ego network: The effect of distant connections on node anonymity Journal Article
In: Scientific Reports, vol. 14, no. 1156 , 2024.
Abstract | Links | BibTeX | Tags:
@article{deJong2024,
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},
doi = {https://doi.org/10.1038/s41598-023-50617-z},
year = {2024},
date = {2024-01-12},
urldate = {2024-01-12},
journal = {Scientific Reports},
volume = {14},
number = {1156 },
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 = {published},
tppubtype = {article}
}
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.
2023
de Jong, Rachel G.; van der Loo, Mark P. J.; Takes, Frank W.
Algorithms for Efficiently Computing Structural Anonymity in Complex Networks Journal Article
In: ACM Journal of Experimental Algorithmics, 2023.
BibTeX | Tags:
@article{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 = {2023-12-23},
urldate = {2023-12-23},
journal = {ACM Journal of Experimental Algorithmics},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
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.