IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-52486-0.html
   My bibliography  Save this article

The efficiency of synchronization dynamics and the role of network syncreactivity

Author

Listed:
  • Amirhossein Nazerian

    (University of New Mexico)

  • Joseph D. Hart

    (US Naval Research Laboratory, Code 5675)

  • Matteo Lodi

    (University of Genoa)

  • Francesco Sorrentino

    (University of New Mexico)

Abstract

Synchronization of coupled oscillators is a fundamental process in both natural and artificial networks. While much work has investigated the asymptotic stability of the synchronous solution, the fundamental question of the transient behavior toward synchronization has received far less attention. In this work, we present the transverse reactivity as a metric to quantify the instantaneous rate of growth or decay of desynchronizing perturbations. We first use the transverse reactivity to design a coupling-efficient and energy-efficient synchronization strategy that involves varying the coupling strength dynamically according to the current state of the system. We find that our synchronization strategy is able to synchronize networks in both simulation and experiment over a significantly larger (often by orders of magnitude) range of coupling strengths than is possible when the coupling strength is constant. Then, we characterize the effects of network topology on the transient dynamics towards synchronization by introducing the concept of network syncreactivity: A network with a larger syncreactivity has a larger transverse reactivity at every point on the synchronization manifold, independent of the oscillator dynamics. We classify real-world examples of complex networks in terms of their syncreactivity.

Suggested Citation

  • Amirhossein Nazerian & Joseph D. Hart & Matteo Lodi & Francesco Sorrentino, 2024. "The efficiency of synchronization dynamics and the role of network syncreactivity," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52486-0
    DOI: 10.1038/s41467-024-52486-0
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-52486-0
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-52486-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. V. E. Demidov & H. Ulrichs & S. V. Gurevich & S. O. Demokritov & V. S. Tiberkevich & A. N. Slavin & A. Zholud & S. Urazhdin, 2014. "Synchronization of spin Hall nano-oscillators to external microwave signals," Nature Communications, Nature, vol. 5(1), pages 1-6, May.
    2. Tengfei Hao & Qizhuang Cen & Yitang Dai & Jian Tang & Wei Li & Jianping Yao & Ninghua Zhu & Ming Li, 2018. "Breaking the limitation of mode building time in an optoelectronic oscillator," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    3. Parastesh, Fatemeh & Azarnoush, Hamed & Jafari, Sajad & Hatef, Boshra & Perc, Matjaž & Repnik, Robert, 2019. "Synchronizability of two neurons with switching in the coupling," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 217-223.
    4. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    5. Apostolos Argyris & Dimitris Syvridis & Laurent Larger & Valerio Annovazzi-Lodi & Pere Colet & Ingo Fischer & Jordi García-Ojalvo & Claudio R. Mirasso & Luis Pesquera & K. Alan Shore, 2005. "Chaos-based communications at high bit rates using commercial fibre-optic links," Nature, Nature, vol. 438(7066), pages 343-346, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    2. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    3. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    4. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    5. Ma, Weiyuan & Bao, Xionggai & Ma, Chenjun, 2024. "Controllability of higher-order networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    6. Meng, Tao & Duan, Gaopeng & Li, Aming & Wang, Long, 2023. "Control energy scaling for target control of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    7. Yin, Haofei & Cui, Xiaohua & Zeng, An, 2024. "An innovative defense strategy against targeted spreading in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
    8. Tao Jia & Robert F Spivey & Boleslaw Szymanski & Gyorgy Korniss, 2015. "An Analysis of the Matching Hypothesis in Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-12, June.
    9. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    10. Zhang, Rui & Wang, Xiaomeng & Cheng, Ming & Jia, Tao, 2019. "The evolution of network controllability in growing networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 257-266.
    11. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    12. Hu, Jingting & Bao, Han & Xu, Quan & Chen, Mo & Bao, Bocheng, 2024. "Synchronization generations and transitions in two map-based neurons coupled with locally active memristor," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    13. Chen, Shi-Ming & Xu, Yun-Fei & Nie, Sen, 2017. "Robustness of network controllability in cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 536-539.
    14. Xizhe Zhang & Huaizhen Wang & Tianyang Lv, 2017. "Efficient target control of complex networks based on preferential matching," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-10, April.
    15. Yu, Xihong & Bao, Han & Chen, Mo & Bao, Bocheng, 2023. "Energy balance via memristor synapse in Morris-Lecar two-neuron network with FPGA implementation," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    16. 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.
    17. Mingjian Li & Tengfei Hao & Guozheng Li & Anle Wang & Yitang Dai & Wei Li & José Capmany & Jianping Yao & Ninghua Zhu & Ming Li, 2024. "Time-variant parity-time symmetry in frequency-scanning systems," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    18. Lu, Lulu & Ge, Mengyan & Xu, Ying & Jia, Ya, 2019. "Phase synchronization and mode transition induced by multiple time delays and noises in coupled FitzHugh–Nagumo model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    19. Xian Xi & Xiangyun Gao & Xiaotian Sun & Huiling Zheng & Congcong Wu, 2024. "Dynamic analysis and application of network structure control in risk conduction in the industrial chain," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    20. Xiao, Guanping & Zheng, Zheng & Wang, Haoqin, 2017. "Evolution of Linux operating system network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 249-258.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52486-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.