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  Asymptotic scaling describing signal propagation in complex networks

Ji, P., Lin, W., Kurths, J. (2020): Asymptotic scaling describing signal propagation in complex networks. - Nature Physics, 16, 11, 1082-1083.
https://doi.org/10.1038/s41567-020-1025-3

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Ji, Peng1, Autor
Lin, Wei1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: The complex dynamics emergent in diverse systems are influenced not only by the network topologies but also by the interplay of these topologies with the dynamical mechanisms of interaction between nodes. Hens et al.1 established a general framework, novelly linking the universal topological features to the spatiotemporal propagation of signals in complex networks and analytically capturing the topological role in predicting local responses through the asymptotic scaling relationship. Although using an appropriate form of the asymptotic scaling can reveal universal characteristics in complex systems, it is likely to lead to biased or even incorrect predictions if the scaling form is not accurately estimated. It is possible, however, to achieve substantial improvements in the predictive power by including a suitable prefactor in the scaling.

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 Datum: 2020-11-022020-11-15
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1038/s41567-020-1025-3
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Organisational keyword: RD4 - Complexity Science
 Art des Abschluß: -

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Titel: Nature Physics
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Seiten: - Band / Heft: 16 (11) Artikelnummer: - Start- / Endseite: 1082 - 1083 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1603091