Collaborations
Throughout the years, our research group has contributed to and collaborated on multiple projects that leverage large-scale network data, advancing both methodological innovation and applied social research.
Some of the most relevant can be found below
City Network Science with O&S Gemeente Amsterdam
The City Network Science Lab is a collaborative project between PLANET-NL and the City of Amsterdam. The lab studies how urban social structures evolve using population-scale social network data. The research focuses on neighborhood connectivity, the social integration of newcomers, and how inequality, vulnerability, and political behavior are reflected in urban networks, with the aim of producing policy-relevant insights that contribute to social cohesion in Amsterdam and the wider metropolitan region. Watch the video to learn more about the work of the lab:
Family networks with Vera de Bel
Together with Vera de Bel (University of Groningen) PLANET-NL examines the kinship layer of the network. The research mainly focuses on the presence and evolution of complex family ties, such as step-grandparenthood, following parental divorce. Vera's work led to the following publication:
de Bel, V., Bokányi, E., Hank, K., & Leopold, T. (2025). A parallel kinship universe? A replication of Kolk et al. (2023) with Dutch register data on kinship networks. Demographic Research, 52, 915–938. https://doi.org/10.4054/DemRes.2025.52.28
Epidemic spread modeling with Kieran Marray
As part of this PhD in econometrics from 2022 to 2024 at the Tinbergen Institute, Kieran Marray, supervised by among others Michael Konig (VU Amsterdam), did a pilot project utilizing the population-scale social network infrastructure developed by the PLANET-NL (formerly POPNET) team. His work uses a geographically aggregated version of the network combined with per-region data on COVID-19 infections to compare different network rewiring strategies to arrive at a model for optimal disease mitigation. His experience in working with the population-scale network data was a profound of example of research-driven infrastructure development, where the continuous development of the mlnliv package and insights from using this code in his research project provided relevant synergistic insights for both the research carried out and the software that was being developed. Kieran's work led to a paper, which, at time of writing this text, is available as preprint:
O. Candogan, M. König, K. Marray and F.W. Takes, Network rewiring and spatial targeting: Optimal disease mitigation in multilayer social networks, BFI Working paper series UChicago, SSRN preprint 5106505, 2025. http://dx.doi.org/10.2139/ssrn.5106505