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Nat Commun. 2018 Jun 27;9(1):2501. doi: 10.1038/s41467-018-04978-z.

From the betweenness centrality in street networks to structural invariants in random planar graphs.

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Department of Physics & Astronomy, University of Rochester, Rochester, NY, 14627, USA.
Institut de Physique Théorique, CEA, CNRS-URA 2306, Gif-sur-Yvette, F-91191, France.
Centre d'Analyse et de Mathématique Sociales (CNRS/EHESS), 54 Boulevard Raspail, Paris, 75006, France.
Department of Physics & Astronomy, University of Rochester, Rochester, NY, 14627, USA.
Goergen Institute for Data Science, University of Rochester, Rochester, NY, 14627, USA.


The betweenness centrality, a path-based global measure of flow, is a static predictor of congestion and load on networks. Here we demonstrate that its statistical distribution is invariant for planar networks, that are used to model many infrastructural and biological systems. Empirical analysis of street networks from 97 cities worldwide, along with simulations of random planar graph models, indicates the observed invariance to be a consequence of a bimodal regime consisting of an underlying tree structure for high betweenness nodes, and a low betweenness regime corresponding to loops providing local path alternatives. Furthermore, the high betweenness nodes display a non-trivial spatial clustering with increasing spatial correlation as a function of the edge-density. Our results suggest that the spatial distribution of betweenness is a more accurate discriminator than its statistics for comparing  static congestion patterns and  its evolution across cities as demonstrated by analyzing 200 years of street data for Paris.

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