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  Fractional core-based collapse mechanism and structural optimization in complex systems

Si, S., Lv, C., Cai, Z., Duan, D., Kurths, J., Wang, Z. (2023): Fractional core-based collapse mechanism and structural optimization in complex systems. - Science China Information Sciences, 66, 192202.
https://doi.org/10.1007/s11432-022-3731-x

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 Creators:
Si, Shubin1, Author
Lv, Changchun1, Author
Cai, Zhiqiang1, Author
Duan, Dongli1, Author
Kurths, Jürgen2, Author              
Wang, Zhen1, Author
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Catastrophic and major disasters in real-world systems ranging from financial markets and ecosystems, often show generic early-warning signals that may indicate a collapse. Hence, understanding the collapse mechanism of a complex network and predicting its process are of uttermost importance. However, these challenges are often hindered by the extremely high dimensionality of the underlying system. We present here the concept of the fractional core (F-core) that considers the contribution of the network topology and dynamics to systematically analyze the collapse process in such networks, and encompass a broad range of dynamical systems, from mutualistic ecosystems to regulatory dynamics. We offer testable predictions on the tipping point, and, in particular, prove that the extinction of the maximum F-core of a network is an efficient indicator of whether a system completely collapses. The results show that the death of species or cells in a low-order F-core may improve the average density and have little influence on the tipping point. Generally, the principle of the F-core demonstrates how complex systems collapse and opens an innovative optimization strategy to uncover the optimal structure of systems.

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Language(s): eng - English
 Dates: 2023-08-282023-08-28
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s11432-022-3731-x
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Tipping Elements
Model / method: Machine Learning
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Science China Information Sciences
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 66 Sequence Number: 192202 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1862-2836
Publisher: Springer