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Diffusion capacity of single and interconnected networks

Author

Listed:
  • Tiago A. Schieber

    (Universidade Federal de Minas Gerais)

  • Laura C. Carpi

    (CEFET-MG
    Universidade Federal de Minas Gerais)

  • Panos M. Pardalos

    (University of Florida
    National Research University, Higher School of Economics)

  • Cristina Masoller

    (Universitat Politècnica de Catalunya)

  • Albert Díaz-Guilera

    (Universitat de Barcelona
    Universitat de Barcelona)

  • Martín G. Ravetti

    (Universidade Federal de Minas Gerais)

Abstract

Understanding diffusive processes in networks is a significant challenge in complexity science. Networks possess a diffusive potential that depends on their topological configuration, but diffusion also relies on the process and initial conditions. This article presents Diffusion Capacity, a concept that measures a node’s potential to diffuse information based on a distance distribution that considers both geodesic and weighted shortest paths and dynamical features of the diffusion process. Diffusion Capacity thoroughly describes the role of individual nodes during a diffusion process and can identify structural modifications that may improve diffusion mechanisms. The article defines Diffusion Capacity for interconnected networks and introduces Relative Gain, which compares the performance of a node in a single structure versus an interconnected one. The method applies to a global climate network constructed from surface air temperature data, revealing a significant change in diffusion capacity around the year 2000, suggesting a loss of the planet’s diffusion capacity that could contribute to the emergence of more frequent climatic events.

Suggested Citation

  • Tiago A. Schieber & Laura C. Carpi & Panos M. Pardalos & Cristina Masoller & Albert Díaz-Guilera & Martín G. Ravetti, 2023. "Diffusion capacity of single and interconnected networks," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37323-0
    DOI: 10.1038/s41467-023-37323-0
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    References listed on IDEAS

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    1. repec:nas:journl:v:115:y:2018:p:e6996-e7004 is not listed on IDEAS
    2. Alexandre Bovet & Hernán A. Makse, 2019. "Influence of fake news in Twitter during the 2016 US presidential election," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
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    Cited by:

    1. Chen, Peng & Qi, Mingze & Yan, Liang & Duan, Xiaojun, 2024. "Diffusion capacity analysis of complex network based on the cluster distribution," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

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