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Cascading crashes induced by the individual heterogeneity in complex networks

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  • Li, Jie
  • Wang, Juan
  • Sun, Shiwen
  • Xia, Chengyi

Abstract

Deep understanding of the birth, growth and evolution of the real-life systems has been widely investigated, but the dynamics of system crashes are far beyond our knowledge. To this end, we propose a dynamical model to illustrate the collapsing behavior of complex networks, in which each node may leave the current networks since it has too few neighbors or has lost more than a specific proportion of its neighboring links. Different from previous works, the probability of being removed from the network for each node will be correlated with its original degree once the leaving conditions are satisfied, which includes the positive or negative correlation with the original degree, and totally independent probability deployment, and the individual heterogeneity has been integrated into these three probability setup schemes. Plenty of numerical simulations have indicated that the leaving probability setup scheme will greatly impact the system crashing behaviors under three different topologies including random, exponential and scale-free networks. In particular, the positively correlated scheme will substantially improve the survival of systems and further enhance the resilience of scale-free networks. To a great degree, the current results can help us to be further acquainted with the crashing dynamics and evolutionary properties of complex systems.

Suggested Citation

  • Li, Jie & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2018. "Cascading crashes induced by the individual heterogeneity in complex networks," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 182-192.
    • Handle: RePEc:eee:apmaco:v:323:y:2018:i:c:p:182-192
    DOI: 10.1016/j.amc.2017.11.059
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    References listed on IDEAS

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    1. Wang, Chengjiang & Wang, Li & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2017. "Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 18-29.
    2. Li, Chao & Wang, Li & Sun, Shiwen & Xia, Chengyi, 2018. "Identification of influential spreaders based on classified neighbors in real-world complex networks," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 512-523.
    3. Quentin Michard & Jean-Philippe Bouchaud, 2005. "Theory of collective opinion shifts: from smooth trends to abrupt swings," Science & Finance (CFM) working paper archive 500060, Science & Finance, Capital Fund Management.
    4. Q. Michard & J.-P. Bouchaud, 2005. "Theory of collective opinion shifts: from smooth trends to abrupt swings," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 47(1), pages 151-159, September.
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    Cited by:

    1. Hu, Jun & Xia, Chengyi & Li, Huijia & Zhu, Peican & Xiong, Wenjun, 2020. "Properties and structural analyses of USA’s regional electricity market: A visibility graph network approach," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    2. Xu Li & Bin Lv & Binke Lang & Qixiang Chen, 2022. "Exploring the Cascading Failure in Taxi Transportation Networks," Sustainability, MDPI, vol. 14(20), pages 1-14, October.
    3. Cáceres, José & Garijo, Delia & González, Antonio & Márquez, Alberto & Puertas, María Luz & Ribeiro, Paula, 2018. "Shortcut sets for the locus of plane Euclidean networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 192-205.
    4. Lv, Changchun & Yuan, Ziwei & Si, Shubin & Duan, Dongli & Yao, Shirui, 2022. "Cascading failure in networks with dynamical behavior against multi-node removal," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    5. Duan, Dongli & Yan, Qi & Rong, Yisheng & Hou, Gege, 2022. "Predicting the cascading failure of dynamical networks based on a new dimension reduction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    6. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
    7. Prasan Ratnayake & Sugandima Weragoda & Janaka Wansapura & Dharshana Kasthurirathna & Mahendra Piraveenan, 2021. "Quantifying the Robustness of Complex Networks with Heterogeneous Nodes," Mathematics, MDPI, vol. 9(21), pages 1-20, November.

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