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

Naseri, N., Parastesh, F., Ghassemi, F., Jafari, S., Schöll, E., Kurths, J. (2022): Converting high-dimensional complex networks to lower-dimensional ones preserving synchronization features. - EPL (Europhysics Letters), 140, 2, 21001.
https://doi.org/10.1209/0295-5075/ac98de

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 Creators:
Naseri, Nafise1, Author
Parastesh, Fatemeh1, Author
Ghassemi, Farnaz1, Author
Jafari, Sajad1, Author
Schöll, Eckehard2, Author              
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: 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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Language(s): eng - English
 Dates: 2022-10-192022-10
 Publication Status: Finally published
 Pages: 7
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1209/0295-5075/ac98de
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: EPL (Europhysics Letters)
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 140 (2) Sequence Number: 21001 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals132
Publisher: IOP Publishing