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Effect of degree correlation on exact controllability of multiplex networks

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  • Nie, Sen
  • Wang, Xuwen
  • Wang, Binghong

Abstract

It has been proved that the degree correlation can affect the structural controllability of directed networks. Here, we explore the effect of interconnections’ correlation on the exact controllability of multiplex networks. We find that the minimal number of driver nodes decreases with correlation for lower density of interconnections. However, the controllability of networks with higher density of interconnections shows the contrary tendency. For different interconnections’ correlations, controllability of multiplex networks depicts transition with the density of interconnections. For lower interconnections density, the networks with disassortative coupling patterns are harder to control. Whereas, for higher interconnections density, the networks with assortative coupling patterns are harder to control.

Suggested Citation

  • Nie, Sen & Wang, Xuwen & Wang, Binghong, 2015. "Effect of degree correlation on exact controllability of multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 98-102.
  • Handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:98-102
    DOI: 10.1016/j.physa.2015.05.038
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    References listed on IDEAS

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    1. Noah J Cowan & Erick J Chastain & Daril A Vilhena & James S Freudenberg & Carl T Bergstrom, 2012. "Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-5, June.
    2. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    3. Zhengzhong Yuan & Chen Zhao & Zengru Di & Wen-Xu Wang & Ying-Cheng Lai, 2013. "Exact controllability of complex networks," Nature Communications, Nature, vol. 4(1), pages 1-9, December.
    4. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2012. "Control Centrality and Hierarchical Structure in Complex Networks," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
    5. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
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    Cited by:

    1. Zhang, Hui & Cui, Houdun & Wang, Wei & Song, Wenbo, 2020. "Properties of Chinese railway network: Multilayer structures based on timetable data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    2. Cui, Pengshuai & Zhu, Peidong & Shao, Chengcheng & Xun, Peng, 2017. "Cascading failures in interdependent networks due to insufficient received support capability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 777-788.

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