Below is a list of the current output of the POPNET project. This list is regularly updated and contains the most recent publicly available output. If you have any questions or comments, please contact us via email.

Anatomy of a Population-scale Social Network
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. 

Lead: Eszter Bokányi
Collaborators: Eelke Heemskerk, Frank Takes, and Yuliia Kazmina
Output: Paper

Socio-economic Segregation in a Population-Scale Social Network

We propose a social network-aware approach to study socio-economic segregation. The key question is whether patterns of segregation in social networks are more pronounced than the common spatial manifestations of segregation. We conduct a population-scale social network analysis to analyze socio-economic segregation at a comprehensive and highly granular level. At the basis of this analysis is high quality register data consisting of complete information on ~17.2 million registered residents of the Netherlands that are connected through ~914 million ties distributed over four distinct tie types. By comparing income assortativity between the social network and the spatial perspective, we find that the social network structure exhibits  a factor of two higher segregation.  This may signal  that while at a particular  scale of spatial aggregation (e.g., the geographical  neighborhood), patterns of socio-economic segregation appear to be minimal,  they in fact persist in the underlying social network structure. Furthermore, we discover higher socioeconomic segregation in larger cities as opposed to a widespread view of cities as hubs for diverse socioeconomic 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.

Lead: Yuliia Kazmina
Collaborators: Eelke Heemskerk, Frank Takes, Eszter Bokanyi
Output: Paper

Algorithms for Efficiently Computing Structural Anonymity in Complex Networks

During this project, we compare various algorithms for computing dk-anonymity. We introduce an iterative step, a twin node preprocessing step and heuristics (graph invariants) to speed up the computation. We compare the different configurations on both graph models (the Erdős-Rényi, Barabási-Albert and the Wats-Strogatz model) and 32 real-world networks with up to 10,000s of nodes.

Lead: Rachel de Jong
Collaborators: Mark van der Loo, Frank Takes
Output: Paper
R.G. de Jong, M.P.J. van der Loo and F.W. Takes, Algorithms for Efficiently Computing Structural Anonymity in Complex Networks, ACM Journal of Experimental Algorithmics, to appear, ACM, 2023.