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Impact of basic network motifs on the collective response to perturbations

Author

Listed:
  • Xiaoge Bao

    (Fudan University
    Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education
    Fudan University)

  • Qitong Hu

    (Fudan University
    Shanghai Jiao Tong University)

  • Peng Ji

    (Fudan University
    Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education
    Fudan University)

  • Wei Lin

    (Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education
    Fudan University
    Fudan University
    Fudan University)

  • Jürgen Kurths

    (Fudan University
    Potsdam Institute for Climate Impact Research
    Humboldt University)

  • Jan Nagler

    (Frankfurt School of Finance & Management
    Frankfurt School of Finance & Management)

Abstract

Many collective phenomena such as epidemic spreading and cascading failures in socioeconomic systems on networks are caused by perturbations of the dynamics. How perturbations propagate through networks, impact and disrupt their functions may depend on the network, the type and location of the perturbation as well as the spreading dynamics. Previous work has analyzed the retardation effects of the nodes along the propagation paths, suggesting a few transient propagation "scaling” regimes as a function of the nodes’ degree, but regardless of motifs such as triangles. Yet, empirical networks consist of motifs enabling the proper functioning of the system. Here, we show that basic motifs along the propagation path jointly determine the previously proposed scaling regimes of distance-limited propagation and degree-limited propagation, or even cease their existence. Our results suggest a radical departure from these scaling regimes and provide a deeper understanding of the interplay of self-dynamics, interaction dynamics, and topological properties.

Suggested Citation

  • Xiaoge Bao & Qitong Hu & Peng Ji & Wei Lin & Jürgen Kurths & Jan Nagler, 2022. "Impact of basic network motifs on the collective response to perturbations," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32913-w
    DOI: 10.1038/s41467-022-32913-w
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    References listed on IDEAS

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    1. Peter J. Menck & Jobst Heitzig & Jürgen Kurths & Hans Joachim Schellnhuber, 2014. "How dead ends undermine power grid stability," Nature Communications, Nature, vol. 5(1), pages 1-8, September.
    2. Christoph Kirst & Marc Timme & Demian Battaglia, 2016. "Dynamic information routing in complex networks," Nature Communications, Nature, vol. 7(1), pages 1-9, April.
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    Cited by:

    1. Wang, Xinyi & Zhang, Xiyun & Zheng, Muhua & Xu, Leijun & Xu, Kesheng, 2023. "Noise-induced coexisting firing patterns in hybrid-synaptic interacting networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    2. Luo, Kaiming & Cai, Zongkai & Liu, Zonghua & Guan, Shuguang & Zou, Yong, 2024. "Effects of uncommon non-isochronicities on remote synchronization," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. Zhou, Xinjia & Tian, Changhai & Zhang, Xiyun & Zheng, Muhua & Xu, Kesheng, 2022. "Short-term plasticity as a mechanism to regulate and retain multistability," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    4. Cambraia, E.B.S.A. & Flauzino, J.V.V. & Prado, T.L. & Lopes, S.R., 2023. "Dependence on the local dynamics of a network phase synchronization process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).

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