Output

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

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

During this project, we compare various algorithms for computing d–k-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.