ArticleNature communications2018
From the betweenness centrality in street networks to structural invariants in random planar graphs.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
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- Distribution of centrality measures on undirected random networks via the cavity method.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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- Dynamic predictability and activity-location contexts in human mobility.Royal Society open science · 2024Article
- Power law of path multiplicity in complex networks.PNAS nexus · 2024Article
- Characterizing network circuity among heterogeneous urban amenities.Journal of the Royal Society, Interface · 2023Article
- A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses.Scientific data · 2023Article
- A link model approach to identify congestion hotspots.Royal Society open science · 2022Article
- Connecting intercity mobility with urban welfare.PNAS nexus · 2022Article
- Growing urban bicycle networks.Scientific reports · 2022Article
- Contrasting social and non-social sources of predictability in human mobility.Nature communications · 2022Article
- A sampling-guided unsupervised learning method to capture percolation in complex networks.Scientific reports · 2022Article
- Impact of urban structure on infectious disease spreading.Scientific reports · 2022Article
- Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value.Computational and mathematical methods in medicine · 2022Article
- Impact of COVID-19 on the mobility patterns: An investigation of taxi trips in Chicago.PloS one · 2022Article
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4 authors.
Funding
Abstract
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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