The anatomy of a population-scale social network

Bokányi, Eszter; Heemskerk, Eelke M.; Takes, Frank W.

doi:10.1038/s41598-023-36324-9

Cited by 12 publications

(1 citation statement)

References 62 publications

(73 reference statements)

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“…Statistics Netherlands recently released the Dutch person network mapping affiliations between individuals (van der Laan et al, 2022). This network contains five layers: family members, housemates, neighbors, classmates, and colleagues (see Bokányi, Heemskerk, et al, 2023 for details on the topological properties of the network). In this commentary, we focus on the family layer to investigate kinship relations among the entire Dutch population (17.2 million individuals), excluding 2.1 million first generation migrants, totaling 15.1 million individuals.…”

Section: Methodsmentioning

confidence: 99%

A parallel kinship universe? Using Dutch kinship network data to replicate Kolk et al.’s (2023) demographic account of kinship networks in Sweden

de Bel,

Bokányi,

Hank

et al. 2024

Preprint

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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.

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Section: Methodsmentioning

confidence: 99%

A parallel kinship universe? Using Dutch kinship network data to replicate Kolk et al.’s (2023) demographic account of kinship networks in Sweden

de Bel,

Bokányi,

Hank

et al. 2024

Preprint

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Predicting COVID-19 infections using multi-layer centrality measures in population-scale networks

Hedde-von Westernhagen,

Bagheri,

Garcia-Bernardo

2024

Appl Netw Sci

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Understanding the spread of SARS-CoV-2 has been one of the most pressing problems of the recent past. Network models present a potent approach to studying such spreading phenomena because of their ability to represent complex social interactions. While previous studies have shown that network centrality measures are generally able to identify influential spreaders in a susceptible population, it is not yet known if they can also be used to predict infection risks. However, information about infection risks at the individual level is vital for the design of targeted interventions. Here, we use large-scale administrative data from the Netherlands to study whether centrality measures can predict the risk and timing of infections with COVID-19-like diseases. We investigate this issue leveraging the framework of multi-layer networks, which accounts for interactions taking place in different contexts, such as workplaces, households and schools. In epidemic models simulated on real-world network data from over one million individuals, we find that existing centrality measures offer good predictions of relative infection risks, and are correlated with the timing of individual infections. We however find no association between centrality measures and real SARS-CoV-2 test data, which indicates that population-scale network data alone cannot aid predictions of virus transmission.

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The complex network patterns of human migration at different geographical scales: network science meets regression analysis

Pitoski,

Meštrović,

Schmeets

2024

Appl Netw Sci

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Migration’s influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of life, it continues to require attention across scientific disciplines. This study aims to bridge the gap between theoretical understanding and practical application by integrating network analysis and regression methodologies within Migration Studies. In the study we employ network analysis to elucidate migration patterns at various geographical scales-city, country, and global. Additionally, regression analysis is discussed on an exploratory level, where we focus on the underlying factors driving migration, and identifying the key independent variables to enhance predictive accuracy. The study exposes distinct migration network structure and its features, and the consequences these have on conventional regression analysis applications. We conclude on the importance of methodological coherence and disciplinary integration, and highlight the avenues for enhancing the predictive power of migration models.

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The anatomy of a population-scale social network

Cited by 12 publications

References 62 publications

A parallel kinship universe? Using Dutch kinship network data to replicate Kolk et al.’s (2023) demographic account of kinship networks in Sweden

A parallel kinship universe? Using Dutch kinship network data to replicate Kolk et al.’s (2023) demographic account of kinship networks in Sweden

Predicting COVID-19 infections using multi-layer centrality measures in population-scale networks

The complex network patterns of human migration at different geographical scales: network science meets regression analysis

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