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Emergence of core–peripheries in networks

Author

Listed:
  • T. Verma

    (ETH Zürich, Computational Physics for Engineering Materials, Institute for Building Materials)

  • F. Russmann

    (ETH Zürich, Computational Physics for Engineering Materials, Institute for Building Materials)

  • N.A.M. Araújo

    (Faculdade de Ciências, Universidade de Lisboa
    Centro de Física Teórica e Computacional, Universidade de Lisboa)

  • J. Nagler

    (ETH Zürich, Computational Physics for Engineering Materials, Institute for Building Materials)

  • H.J. Herrmann

    (ETH Zürich, Computational Physics for Engineering Materials, Institute for Building Materials
    Universidade Federal do Ceará)

Abstract

A number of important transport networks, such as the airline and trade networks of the world, exhibit a characteristic core–periphery structure, wherein a few nodes are highly interconnected and the rest of the network frays into a tree. Mechanisms underlying the emergence of core–peripheries, however, remain elusive. Here, we demonstrate that a simple pruning process based on removal of underutilized links and redistribution of loads can lead to the emergence of core–peripheries. Links are assumed beneficial if they either carry a sufficiently large load or are essential for global connectivity. This incentivized redistribution process is controlled by a single parameter, which balances connectivity and profit. The obtained networks exhibit a highly resilient and connected core with a frayed periphery. The balanced network shows a higher resilience than the world airline network or the world trade network, revealing a pathway towards robust structural features through pruning.

Suggested Citation

  • T. Verma & F. Russmann & N.A.M. Araújo & J. Nagler & H.J. Herrmann, 2016. "Emergence of core–peripheries in networks," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
  • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10441
    DOI: 10.1038/ncomms10441
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    Cited by:

    1. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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