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协同进化网络中非线性相互作用和噪声产生的复杂结构。

Emergence of complex structures from nonlinear interactions and noise in coevolving networks.

机构信息

Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Pasteura 5, 02-093, Warsaw, Poland.

IFISC, Institute for Cross-disciplinary Physics and Complex Systems (UIB-CSIC), Campus Universitat Illes Balears, 07122, Palma de Mallorca, Spain.

出版信息

Sci Rep. 2020 Sep 24;10(1):15660. doi: 10.1038/s41598-020-72662-8.

DOI:10.1038/s41598-020-72662-8
PMID:32973287
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7519106/
Abstract

We study the joint effect of the non-linearity of interactions and noise on coevolutionary dynamics. We choose the coevolving voter model as a prototype framework for this problem. By numerical simulations and analytical approximations we find three main phases that differ in the absolute magnetisation and the size of the largest component: a consensus phase, a coexistence phase, and a dynamical fragmentation phase. More detailed analysis reveals inner differences in these phases, allowing us to divide two of them further. In the consensus phase we can distinguish between a weak or alternating consensus and a strong consensus, in which the system remains in the same state for the whole realisation of the stochastic dynamics. In the coexistence phase we distinguish a fully-mixing phase and a structured coexistence phase, where the number of active links drops significantly due to the formation of two homogeneous communities. Our numerical observations are supported by an analytical description using a pair approximation approach and an ad-hoc calculation for the transition between the coexistence and dynamical fragmentation phases. Our work shows how simple interaction rules including the joint effect of non-linearity, noise, and coevolution lead to complex structures relevant in the description of social systems.

摘要

我们研究了相互作用的非线性和噪声对协同进化动力学的联合影响。我们选择共同进化的投票模型作为这个问题的原型框架。通过数值模拟和分析近似,我们发现了三个主要的相位,它们在绝对磁化强度和最大分量的大小上有所不同:共识相位、共存相位和动态分裂相位。更详细的分析揭示了这些相位的内在差异,使我们能够进一步将其中两个相位进一步划分。在共识相位中,我们可以区分弱或交替共识和强共识,在强共识中,系统在随机动力学的整个实现过程中保持相同的状态。在共存相位中,我们区分了完全混合相和结构共存相,其中由于两个均匀社区的形成,活动链接的数量显著下降。我们的数值观察得到了使用配对近似方法和特定于共存和动态分裂相之间的过渡的计算的分析描述的支持。我们的工作表明,包括非线性、噪声和共同进化的联合影响在内的简单相互作用规则如何导致在描述社会系统时相关的复杂结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/9cb90d8357d6/41598_2020_72662_Fig12_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/9cb90d8357d6/41598_2020_72662_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/7ac95a520301/41598_2020_72662_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/3c04fdda7ceb/41598_2020_72662_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/d428ea76d0a5/41598_2020_72662_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/7ef873d3b4c9/41598_2020_72662_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/b07f6b962551/41598_2020_72662_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/b135268161af/41598_2020_72662_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/3a6dc1cad422/41598_2020_72662_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/26ef7436d84f/41598_2020_72662_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/84ec71336562/41598_2020_72662_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/0bf5c28db705/41598_2020_72662_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/9bb3662bd237/41598_2020_72662_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c74a/7519106/9cb90d8357d6/41598_2020_72662_Fig12_HTML.jpg

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本文引用的文献

1
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2
Predicting language diversity with complex networks.用复杂网络预测语言多样性。
PLoS One. 2018 Apr 27;13(4):e0196593. doi: 10.1371/journal.pone.0196593. eCollection 2018.
3
Coupling of link- and node-ordering in the coevolving voter model.链接和节点排序在共演化投票模型中的耦合。
Phys Rev E. 2017 Oct;96(4-1):042306. doi: 10.1103/PhysRevE.96.042306. Epub 2017 Oct 18.
4
Fragmentation transitions in a coevolving nonlinear voter model.共进化非线性选民模型中的碎片化转变
Sci Rep. 2017 Oct 9;7(1):12864. doi: 10.1038/s41598-017-13047-2.
5
Rescue of endemic states in interconnected networks with adaptive coupling.具有自适应耦合的互联网络中地方病状态的挽救。
Sci Rep. 2016 Jul 6;6:29342. doi: 10.1038/srep29342.
6
The noisy voter model on complex networks.复杂网络上的噪声选民模型。
Sci Rep. 2016 Apr 20;6:24775. doi: 10.1038/srep24775.
7
Markets, Herding and Response to External Information.市场、羊群效应与对外部信息的反应
PLoS One. 2015 Jul 23;10(7):e0133287. doi: 10.1371/journal.pone.0133287. eCollection 2015.
8
Is the voter model a model for voters?投票者模型是针对投票者的模型吗?
Phys Rev Lett. 2014 Apr 18;112(15):158701. doi: 10.1103/PhysRevLett.112.158701.
9
The spread of behavior in an online social network experiment.在线社交网络实验中的行为传播。
Science. 2010 Sep 3;329(5996):1194-7. doi: 10.1126/science.1185231.
10
Generic absorbing transition in coevolution dynamics.协同进化动力学中的一般吸收转变。
Phys Rev Lett. 2008 Mar 14;100(10):108702. doi: 10.1103/PhysRevLett.100.108702.