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Network isolators inhibit failure spreading in complex networks

Author

Listed:
  • Franz Kaiser

    (Forschungszentrum Jülich, Institute for Energy and Climate Research (IEK-STE)
    University of Cologne)

  • Vito Latora

    (Queen Mary University of London
    Università di Catania and INFN
    The Alan Turing Institute, The British Library
    Complexity Science Hub Vienna)

  • Dirk Witthaut

    (Forschungszentrum Jülich, Institute for Energy and Climate Research (IEK-STE)
    University of Cologne)

Abstract

In our daily lives, we rely on the proper functioning of supply networks, from power grids to water transmission systems. A single failure in these critical infrastructures can lead to a complete collapse through a cascading failure mechanism. Counteracting strategies are thus heavily sought after. In this article, we introduce a general framework to analyse the spreading of failures in complex networks and demostrate that not only decreasing but also increasing the connectivity of the network can be an effective method to contain damages. We rigorously prove the existence of certain subgraphs, called network isolators, that can completely inhibit any failure spreading, and we show how to create such isolators in synthetic and real-world networks. The addition of selected links can thus prevent large scale outages as demonstrated for power transmission grids.

Suggested Citation

  • Franz Kaiser & Vito Latora & Dirk Witthaut, 2021. "Network isolators inhibit failure spreading in complex networks," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23292-9
    DOI: 10.1038/s41467-021-23292-9
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    Cited by:

    1. Qu, Junyi & Liu, Ying & Tang, Ming & Guan, Shuguang, 2022. "Identification of the most influential stocks in financial networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    2. Franz Kaiser & Philipp C. Böttcher & Henrik Ronellenfitsch & Vito Latora & Dirk Witthaut, 2022. "Dual communities in spatial networks," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Ai, Jun & He, Tao & Su, Zhan & Shang, Lihui, 2022. "Identifying influential nodes in complex networks based on spreading probability," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    4. Yin, Haofei & Zhang, Aobo & Zeng, An, 2023. "Identifying hidden target nodes for spreading in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).

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