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振荡器网络中的图划分与集群同步

Graph partitions and cluster synchronization in networks of oscillators.

作者信息

Schaub Michael T, O'Clery Neave, Billeh Yazan N, Delvenne Jean-Charles, Lambiotte Renaud, Barahona Mauricio

机构信息

ICTEAM, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium.

Center for International Development, Harvard University, Cambridge, Maasachusetts 02138, USA.

出版信息

Chaos. 2016 Sep;26(9):094821. doi: 10.1063/1.4961065.

Abstract

Synchronization over networks depends strongly on the structure of the coupling between the oscillators. When the coupling presents certain regularities, the dynamics can be coarse-grained into clusters by means of External Equitable Partitions of the network graph and their associated quotient graphs. We exploit this graph-theoretical concept to study the phenomenon of cluster synchronization, in which different groups of nodes converge to distinct behaviors. We derive conditions and properties of networks in which such clustered behavior emerges and show that the ensuing dynamics is the result of the localization of the eigenvectors of the associated graph Laplacians linked to the existence of invariant subspaces. The framework is applied to both linear and non-linear models, first for the standard case of networks with positive edges, before being generalized to the case of signed networks with both positive and negative interactions. We illustrate our results with examples of both signed and unsigned graphs for consensus dynamics and for partial synchronization of oscillator networks under the master stability function as well as Kuramoto oscillators.

摘要

网络同步在很大程度上取决于振荡器之间耦合的结构。当耦合呈现出一定规律时,通过网络图的外部公平划分及其相关商图,动力学可以被粗粒化为簇。我们利用这一图论概念来研究簇同步现象,即不同组的节点收敛到不同的行为。我们推导了出现这种簇状行为的网络的条件和性质,并表明随之而来的动力学是与不变子空间的存在相关联的图拉普拉斯算子特征向量局部化的结果。该框架既适用于线性模型,也适用于非线性模型,首先用于具有正边的网络的标准情况,然后推广到具有正负相互作用的带符号网络的情况。我们用带符号和无符号图的例子来说明我们的结果,这些例子涉及共识动力学以及在主稳定性函数下振荡器网络的部分同步,还有库拉索振荡器。

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本文引用的文献

1
Complete characterization of the stability of cluster synchronization in complex dynamical networks.
Sci Adv. 2016 Apr 22;2(4):e1501737. doi: 10.1126/sciadv.1501737. eCollection 2016 Apr.
2
Emergence of Slow-Switching Assemblies in Structured Neuronal Networks.
PLoS Comput Biol. 2015 Jul 15;11(7):e1004196. doi: 10.1371/journal.pcbi.1004196. eCollection 2015 Jul.
5
Observability and coarse graining of consensus dynamics through the external equitable partition.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Oct;88(4):042805. doi: 10.1103/PhysRevE.88.042805. Epub 2013 Oct 11.
6
Remote synchronization reveals network symmetries and functional modules.
Phys Rev Lett. 2013 Apr 26;110(17):174102. doi: 10.1103/PhysRevLett.110.174102. Epub 2013 Apr 25.
7
Dynamical models explaining social balance and evolution of cooperation.
PLoS One. 2013 Apr 25;8(4):e60063. doi: 10.1371/journal.pone.0060063. Print 2013.
8
Control of synchronization patterns in neural-like Boolean networks.
Phys Rev Lett. 2013 Mar 8;110(10):104102. doi: 10.1103/PhysRevLett.110.104102. Epub 2013 Mar 5.
9
Experimental observations of group synchrony in a system of chaotic optoelectronic oscillators.
Phys Rev Lett. 2013 Feb 8;110(6):064104. doi: 10.1103/PhysRevLett.110.064104. Epub 2013 Feb 6.
10
Cluster and group synchronization in delay-coupled networks.
Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Jul;86(1 Pt 2):016202. doi: 10.1103/PhysRevE.86.016202. Epub 2012 Jul 5.

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