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Realistic control of network dynamics

Author

Listed:
  • Sean P. Cornelius

    (Northwestern University)

  • William L. Kath

    (Northwestern University
    Northwestern Institute on Complex Systems, Northwestern University)

  • Adilson E. Motter

    (Northwestern University
    Northwestern Institute on Complex Systems, Northwestern University)

Abstract

The control of complex networks is of paramount importance in areas as diverse as ecosystem management, emergency response and cell reprogramming. A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behaviour or fail. Here we show that it is possible to exploit the same principle to control network behaviour. Our approach accounts for the nonlinear dynamics inherent to real systems, and allows bringing the system to a desired target state even when this state is not directly accessible due to constraints that limit the allowed interventions. Applications show that this framework permits reprogramming a network to a desired task, as well as rescuing networks from the brink of failure—which we illustrate through the mitigation of cascading failures in a power-grid network and the identification of potential drug targets in a signalling network of human cancer.

Suggested Citation

  • Sean P. Cornelius & William L. Kath & Adilson E. Motter, 2013. "Realistic control of network dynamics," Nature Communications, Nature, vol. 4(1), pages 1-9, October.
    • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms2939
    DOI: 10.1038/ncomms2939
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    Citations

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    Cited by:

    1. Chen, Lei & Yue, Dong & Dou, Chunxia, 2019. "Optimization on vulnerability analysis and redundancy protection in interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1216-1226.
    2. Sun, Peng Gang & Ma, Xiaoke & Chi, Juan, 2017. "Dominating complex networks by identifying minimum skeletons," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 182-191.
    3. Dingjie Wang & Suoqin Jin & Fang-Xiang Wu & Xiufen Zou, 2015. "Estimation Of Control Energy And Control Strategies For Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(07n08), pages 1-23, November.
    4. Aming Li & Yang-Yu Liu, 2020. "Controlling Network Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-19, February.
    5. Pang, Shao-Peng & Hao, Fei, 2018. "Effect of interaction strength on robustness of controlling edge dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 246-257.
    6. Luboš Brim & Samuel Pastva & David Šafránek & Eva Šmijáková, 2021. "Parallel One-Step Control of Parametrised Boolean Networks," Mathematics, MDPI, vol. 9(5), pages 1-16, March.
    7. Yan Zhang & Frank Schweitzer, 2021. "Quantifying the importance of firms by means of reputation and network control," Papers 2101.05010, arXiv.org.
    8. Dingjie Wang & Xiufen Zou, 2017. "Control Energy And Controllability Of Multilayer Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(04n05), pages 1-25, June.
    9. Wildemeersch, Matthias & Franklin, Oskar & Seidl, Rupert & Rogelj, Joeri & Moorthy, Inian & Thurner, Stefan, 2019. "Modelling the multi-scaled nature of pest outbreaks," Ecological Modelling, Elsevier, vol. 409(C), pages 1-1.
    10. Gaopeng Duan & Aming Li & Tao Meng & Long Wang, 2020. "Energy Cost For Target Control Of Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-27, March.
    11. Hayato Goto & Hideki Takayasu & Misako Takayasu, 2017. "Estimating risk propagation between interacting firms on inter-firm complex network," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-12, October.

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