ArticleScientific reports2017
Physical controllability of complex networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- The frequency response of networks as open systems.Nature communications · 2026Article
- Low-dimensional controllability of brain networks.PLoS computational biology · 2025Article
- Resonant learning in scale-free networks.PLoS computational biology · 2023Article
- Signaling network analysis reveals fostamatinib as a potential drug to control platelet hyperactivation during SARS-CoV-2 infection.Frontiers in immunology · 2023Article
- Article
- Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.Frontiers in computational neuroscience · 2023Article
- Data-driven control of complex networks.Nature communications · 2021Article
- Optimizing target nodes selection for the control energy of directed complex networks.Scientific reports · 2020Article
- On the effects of memory and topology on the controllability of complex dynamical networks.Scientific reports · 2020Article
- Harnessing tipping points in complex ecological networks.Journal of the Royal Society, Interface · 2019Article
- Irrelevance of linear controllability to nonlinear dynamical networks.Nature communications · 2019Article
- Enhancing Controllability Robustness ofResearch (Washington, D.C.) · 2019Article
- Structural, dynamical and symbolic observability: From dynamical systems to networks.PloS one · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.