ArticleNature communications2019
Irrelevance of linear controllability to nonlinear dynamical networks.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Quantifying State-Dependent Control Properties of Brain Dynamics from Perturbation Responses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Model-free tracking control of complex dynamical trajectories with machine learning.Nature communications · 2023Article
- Impact of White Adipose Tissue on Brain Structure, Perfusion, and Cognitive Function in Patients With Severe Obesity: The BARICO Study.Neurology · 2023Article
- Global stabilizing control of large-scale biomolecular regulatory networks.Bioinformatics (Oxford, England) · 2023Article
- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- The impact of aging on human brain network target controllability.Brain structure & function · 2022Article
- NETISCE: a network-based tool for cell fate reprogramming.NPJ systems biology and applications · 2022Article
- Fixed Point Attractor Theory Bridges Structure and Function inFrontiers in neuroscience · 2022Article
- The missing role of gray matter in studying brain controllability.Network neuroscience (Cambridge, Mass.) · 2021Article
- Control of brain network dynamics across diverse scales of space and time.Physical review. E · 2020Article
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Authors and funding
2 authors.
Funding
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Abstract
There has been tremendous development in linear controllability of complex networks. Real-world systems are fundamentally nonlinear. Is linear controllability relevant to nonlinear dynamical networks? We identify a common trait underlying both types of control: the nodal "importance". For nonlinear and linear control, the importance is determined, respectively, by physical/biological considerations and the probability for a node to be in the minimum driver set. We study empirical mutualistic networks and a gene regulatory network, for which the nonlinear nodal importance can be quantified by the ability of individual nodes to restore the system from the aftermath of a tipping-point transition. We find that the nodal importance ranking for nonlinear and linear control exhibits opposite trends: for the former large-degree nodes are more important but for the latter, the importance scale is tilted towards the small-degree nodes, suggesting strongly the irrelevance of linear controllability to these systems. The recent claim of successful application of linear controllability to Caenorhabditis elegans connectome is examined and discussed.
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