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  Dynamical network size estimation from local observations

Tang, X., Huo, W., Yuan, Y., Li, X., Shi, L., Ding, H., Kurths, J. (2020): Dynamical network size estimation from local observations. - New Journal of Physics, 22, 093031.
https://doi.org/10.1088/1367-2630/abaf2f

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 Urheber:
Tang, Xiuchuan1, Autor
Huo, Wei1, Autor
Yuan, Ye1, Autor
Li, Xiuting1, Autor
Shi, Ling1, Autor
Ding, Han1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Here we present a method to estimate the total number of nodes of a network using locally observed response dynamics. The algorithm has the following advantages: (a) it is data-driven. Therefore it does not require any prior knowledge about the model; (b) it does not need to collect measurements from multiple stimulus; and (c) it is distributed as it uses local information only, without any prior information about the global network. Even if only a single node is measured, the exact network size can be correctly estimated using a single trajectory. The proposed algorithm has been applied to both linear and nonlinear networks in simulation, illustrating the applicability to real-world physical networks.

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 Datum: 2020-08-122020-09-142020
 Publikationsstatus: Final veröffentlicht
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1088/1367-2630/abaf2f
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
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Titel: New Journal of Physics
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, oa
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Seiten: - Band / Heft: 22 Artikelnummer: 093031 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1911272
Publisher: IOP Publishing