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Optimal percolation on multiplex networks

Author

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  • Saeed Osat

    (Molecular Simulation Laboratory, Department of Physics, Faculty of Basic Sciences, Azarbaijan Shahid Madani University
    Quantum Complexity Science Initiative, Skolkovo Institute of Science and Technology)

  • Ali Faqeeh

    (Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University)

  • Filippo Radicchi

    (Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University)

Abstract

Optimal percolation is the problem of finding the minimal set of nodes whose removal from a network fragments the system into non-extensive disconnected clusters. The solution to this problem is important for strategies of immunization in disease spreading, and influence maximization in opinion dynamics. Optimal percolation has received considerable attention in the context of isolated networks. However, its generalization to multiplex networks has not yet been considered. Here we show that approximating the solution of the optimal percolation problem on a multiplex network with solutions valid for single-layer networks extracted from the multiplex may have serious consequences in the characterization of the true robustness of the system. We reach this conclusion by extending many of the methods for finding approximate solutions of the optimal percolation problem from single-layer to multiplex networks, and performing a systematic analysis on synthetic and real-world multiplex networks.

Suggested Citation

  • Saeed Osat & Ali Faqeeh & Filippo Radicchi, 2017. "Optimal percolation on multiplex networks," Nature Communications, Nature, vol. 8(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01442-2
    DOI: 10.1038/s41467-017-01442-2
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    Cited by:

    1. Liang, Yuan & Qi, Mingze & Huangpeng, Qizi & Duan, Xiaojun, 2023. "Percolation of interlayer feature-correlated multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Thomas Parmer & Luis M. Rocha & Filippo Radicchi, 2022. "Influence maximization in Boolean networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Quan Ye & Guanghui Yan & Wenwen Chang & Hao Luo, 2023. "Vital node identification based on cycle structure in a multiplex network," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(2), pages 1-16, February.
    4. Osat, Saeed & Radicchi, Filippo, 2018. "Observability transition in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 745-761.
    5. Song, Le & Ma, Yinghong, 2022. "Evaluating tacit knowledge diffusion with algebra matrix algorithm based social networks," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    6. Wang, Ning & Jin, Zi-Yang & Zhao, Jiao, 2021. "Cascading failures of overload behaviors on interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    7. Fan, Dongming & Sun, Bo & Dui, Hongyan & Zhong, Jilong & Wang, Ziyao & Ren, Yi & Wang, Zili, 2022. "A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    8. Benjamin Steinegger & Iacopo Iacopini & Andreia Sofia Teixeira & Alberto Bracci & Pau Casanova-Ferrer & Alberto Antonioni & Eugenio Valdano, 2022. "Non-selective distribution of infectious disease prevention may outperform risk-based targeting," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    9. Zhao, Dawei & Wang, Lianhai & Xu, Shujiang & Liu, Guangqi & Han, Xiaohui & Li, Shudong, 2017. "Vital layer nodes of multiplex networks for immunization and attack," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 169-175.

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