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Structural permeability of complex networks to control signals

Author

Listed:
  • Francesco Lo Iudice

    (University of Naples Federico II
    University of New Mexico)

  • Franco Garofalo

    (University of Naples Federico II)

  • Francesco Sorrentino

    (University of New Mexico)

Abstract

Many biological, social and technological systems can be described as complex networks. The goal of affecting their behaviour has motivated recent work focusing on the relationship between the network structure and its propensity to be controlled. While this work has provided insight into several relevant problems, a comprehensive approach to address partial and complete controllability of networks is still lacking. Here, we bridge this gap by developing a framework to maximize the diffusion of the control signals through a network, while taking into account physical and economic constraints that inevitably arise in applications. This approach allows us to introduce the network permeability, a unified metric of the propensity of a network to be controllable. The analysis of the permeability of several synthetic and real networks enables us to extract some structural features that deepen our quantitative understanding of the ease with which specific controllability requirements can be met.

Suggested Citation

  • Francesco Lo Iudice & Franco Garofalo & Francesco Sorrentino, 2015. "Structural permeability of complex networks to control signals," Nature Communications, Nature, vol. 6(1), pages 1-6, December.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9349
    DOI: 10.1038/ncomms9349
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    Cited by:

    1. Li, Xin-Feng & Lu, Zhe-Ming, 2016. "Optimizing the controllability of arbitrary networks with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 422-433.
    2. Pang, Shao-Peng & Hao, Fei, 2018. "Target control of edge dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 14-26.

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