A self-supervised GNN trained on census commute flows yields urban community delineations that match modularity-based methods in spatial coherence and reveal income-segregated neighborhoods in 12 U.S. metro areas.
Redrawing the map of Great Britain from a network of human interactions
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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities
A self-supervised GNN trained on census commute flows yields urban community delineations that match modularity-based methods in spatial coherence and reveal income-segregated neighborhoods in 12 U.S. metro areas.