Population-Scale Network Embeddings Expose Educational Divides in Network Structure Related to Right-Wing Populist Voting
This work addresses the challenge of interpreting large-scale social network data for political science, though it is incremental as it builds on existing embedding methods with modest predictive gains.
The study tackled the problem of predicting right-wing populist voting using population-scale network embeddings derived from Dutch administrative data, finding that embeddings alone predicted above chance but worse than individual characteristics, and combining them only slightly improved predictions. After transforming embeddings for interpretability, one dimension revealed structural network differences related to education levels and school ties associated with voting behavior.
Administrative registry data can be used to construct population-scale networks whose ties reflect shared social contexts between persons. With machine learning, such networks can be encoded into numerical representations -- embeddings -- that automatically capture individuals' position within the network. We created embeddings for all persons in the Dutch population from a population-scale network that represents five shared contexts: neighborhood, work, family, household, and school. To assess the informativeness of these embeddings, we used them to predict right-wing populist voting. Embeddings alone predicted right-wing populist voting above chance-level but performed worse than individual characteristics. Combining the best subset of embeddings with individual characteristics only slightly improved predictions. After transforming the embeddings to make their dimensions more sparse and orthogonal, we found that one embedding dimension was strongly associated with the outcome. Mapping this dimension back to the population network revealed differences in network structure related to right-wing populist voting between different school ties and achieved education levels. Our study contributes methodologically by demonstrating how population-scale network embeddings can be made interpretable, and substantively by linking structural network differences in education to right-wing populist voting.