An optimization method to keep synchronization features when decreasing network nodes

Naseri, Nafise; Parastesh, Fatemeh; Karami, Mahdi; Ghassemi, Farnaz

doi:10.1140/epjs/s11734-022-00626-2

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…Reducing the dimension of large-scale networks helps decrease the computational complexity and required memory. The paper [18] presents an optimization algorithm to preserve synchronization after node reduction. The proposed algorithm tries to keep the eigenvalues of the network unchanged while reducing the network size.…”

Section: Introductionmentioning

confidence: 99%

Collective behavior of nonlinear dynamical oscillators

Jafari

Bao

Volos

et al. 2022

Eur. Phys. J. Spec. Top.

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Section: Introductionmentioning

confidence: 99%

Collective behavior of nonlinear dynamical oscillators

Jafari

Bao

Volos

et al. 2022

Eur. Phys. J. Spec. Top.

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“…Recently, different methods such as Spectral Coarse-Graining [28] and a Search Algorithm to Dimension Reduction [29] have been proposed. These algorithms decrease the dimension of the Laplacian matrix of the graph, while preserving some specific features of the parent network to keep synchronization.…”

mentioning

confidence: 99%

“…These algorithms decrease the dimension of the Laplacian matrix of the graph, while preserving some specific features of the parent network to keep synchronization. Whereas Spectral Coarse-Graining [28] iteratively reduces the dimension by merging the nodes, the Search Algorithm [29] can effectively reduce the number of nodes through a fast search. Another systematic approach for size reduction has been taken into account recently.…”

mentioning

confidence: 99%

Converting high-dimensional complex networks to lower-dimensional ones preserving synchronization features

Naseri

Parastesh

Ghassemi

et al. 2022

EPL

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Studying the stability of synchronization of coupled oscillators is one of the prominent topics in network science. However, in most cases, the computational cost of complex network analysis is challenging because they consist of a large number of nodes. This study includes overcoming this obstacle by presenting a method for reducing the dimension of a large-scale network, while keeping the complete region of stable synchronization unchanged. To this aim, the first and last non-zero eigenvalues of the Laplacian matrix of a large network are preserved using the eigen-decomposition method and, Gram-Schmidt orthogonalization. The method is only applicable to undirected networks and the result is a weighted undirected network with smaller size. The reduction method is studied in a large-scale a small-world network of Sprott-B oscillators. The results show that the trend of the synchronization error is well maintained after node reduction for different coupling schemes.

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Hamiltonian energy analysis of a multilayer Hindmarsh–Rose neuronal network

Lei,

Parastesh,

et al. 2024

Nonlinear Dyn

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An optimization method to keep synchronization features when decreasing network nodes

Cited by 4 publications

References 35 publications

Collective behavior of nonlinear dynamical oscillators

Collective behavior of nonlinear dynamical oscillators

Converting high-dimensional complex networks to lower-dimensional ones preserving synchronization features

Hamiltonian energy analysis of a multilayer Hindmarsh–Rose neuronal network

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