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相关性对网络可控性的影响。

Effect of correlations on network controllability.

机构信息

Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA, USA.

出版信息

Sci Rep. 2013;3:1067. doi: 10.1038/srep01067. Epub 2013 Jan 15.

DOI:10.1038/srep01067
PMID:23323210
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3545232/
Abstract

A dynamical system is controllable if by imposing appropriate external signals on a subset of its nodes, it can be driven from any initial state to any desired state in finite time. Here we study the impact of various network characteristics on the minimal number of driver nodes required to control a network. We find that clustering and modularity have no discernible impact, but the symmetries of the underlying matching problem can produce linear, quadratic or no dependence on degree correlation coefficients, depending on the nature of the underlying correlations. The results are supported by numerical simulations and help narrow the observed gap between the predicted and the observed number of driver nodes in real networks.

摘要

如果通过对系统的一个子集施加适当的外部信号,一个动态系统在有限的时间内可以从任何初始状态驱动到任何期望的状态,那么这个系统就是可控的。在这里,我们研究了各种网络特性对控制网络所需的最小驱动节点数的影响。我们发现,聚类和模块性没有明显的影响,但基础匹配问题的对称性可以产生线性、二次或与度相关系数无关的依赖关系,这取决于基础相关性的性质。这些结果得到了数值模拟的支持,并有助于缩小在真实网络中预测的和观察到的驱动节点数之间的差距。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/e721079e6f42/srep01067-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/6f444562320b/srep01067-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/b20565728154/srep01067-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/131283c2c6d4/srep01067-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/f5e03936fd97/srep01067-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/a5d71967f97b/srep01067-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/810cbbb63aae/srep01067-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/e721079e6f42/srep01067-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/6f444562320b/srep01067-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/b20565728154/srep01067-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/131283c2c6d4/srep01067-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/f5e03936fd97/srep01067-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/a5d71967f97b/srep01067-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/810cbbb63aae/srep01067-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd0/3545232/e721079e6f42/srep01067-f7.jpg

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本文引用的文献

1
Controlling complex networks: how much energy is needed?控制复杂网络:需要多少能量?
Phys Rev Lett. 2012 May 25;108(21):218703. doi: 10.1103/PhysRevLett.108.218703. Epub 2012 May 23.
2
Targeting the dynamics of complex networks.靶向复杂网络的动态。
Sci Rep. 2012;2:396. doi: 10.1038/srep00396. Epub 2012 May 4.
3
Hierarchy measure for complex networks.复杂网络的层次度量。
分子多重网络网络控制中的结构特征。
PLoS One. 2023 Mar 30;18(3):e0283768. doi: 10.1371/journal.pone.0283768. eCollection 2023.
4
Input node placement restricting the longest control chain in controllability of complex networks.输入节点位置限制复杂网络可控性中最长控制链。
Sci Rep. 2023 Mar 7;13(1):3752. doi: 10.1038/s41598-023-30810-w.
5
Network inference from perturbation time course data.从扰动时间过程数据中进行网络推断。
NPJ Syst Biol Appl. 2022 Nov 1;8(1):42. doi: 10.1038/s41540-022-00253-6.
6
Dilations and degeneracy in network controllability.网络可控性中的扩张和简并。
Sci Rep. 2021 May 5;11(1):9568. doi: 10.1038/s41598-021-88529-5.
7
Steering complex networks toward desired dynamics.引导复杂网络朝着期望的动态发展。
Sci Rep. 2020 Nov 27;10(1):20744. doi: 10.1038/s41598-020-77663-1.
8
Topology Effects on Sparse Control of Complex Networks with Laplacian Dynamics.拉普拉斯动力学下复杂网络稀疏控制的拓扑效应
Sci Rep. 2019 Jun 21;9(1):9034. doi: 10.1038/s41598-019-45476-6.
9
Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree.基于网络距离的模拟退火和模糊聚类用于传感器布置,以确保可观测性和最小相对度。
Sensors (Basel). 2018 Sep 14;18(9):3096. doi: 10.3390/s18093096.
10
Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes.胰岛特异性调控网络中的可控性鉴定出了转录因子NFATC4,该因子调控与2型糖尿病相关的基因。
NPJ Syst Biol Appl. 2018 Jul 3;4:25. doi: 10.1038/s41540-018-0057-0. eCollection 2018.
PLoS One. 2012;7(3):e33799. doi: 10.1371/journal.pone.0033799. Epub 2012 Mar 28.
4
Optimizing controllability of complex networks by minimum structural perturbations.通过最小结构扰动优化复杂网络的可控性
Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Feb;85(2 Pt 2):026115. doi: 10.1103/PhysRevE.85.026115. Epub 2012 Feb 22.
5
Controllability of complex networks.复杂网络的控制
Nature. 2011 May 12;473(7346):167-73. doi: 10.1038/nature10011.
6
Link communities reveal multiscale complexity in networks.链接社区揭示了网络的多尺度复杂性。
Nature. 2010 Aug 5;466(7307):761-4. doi: 10.1038/nature09182. Epub 2010 Jun 20.
7
Edge direction and the structure of networks.边缘方向和网络结构。
Proc Natl Acad Sci U S A. 2010 Jun 15;107(24):10815-20. doi: 10.1073/pnas.0912671107. Epub 2010 May 26.
8
Asymptotic properties of degree-correlated scale-free networks.度相关无标度网络的渐近性质。
Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Apr;81(4 Pt 2):046103. doi: 10.1103/PhysRevE.81.046103. Epub 2010 Apr 9.
9
Community structure in directed networks.有向网络中的群落结构。
Phys Rev Lett. 2008 Mar 21;100(11):118703. doi: 10.1103/PhysRevLett.100.118703.
10
Controllability of complex networks via pinning.通过牵制实现复杂网络的可控性
Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Apr;75(4 Pt 2):046103. doi: 10.1103/PhysRevE.75.046103. Epub 2007 Apr 3.