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  Adaptive dynamical networks

Berner, R., Gross, T., Kuehn, C., Kurths, J., Yanchuk, S. (2023): Adaptive dynamical networks. - Physics Reports, 1031, 1-59.
https://doi.org/10.1016/j.physrep.2023.08.001

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 Urheber:
Berner, Rico1, Autor
Gross, Thilo1, Autor
Kuehn, Christian1, Autor
Kurths, Jürgen2, Autor              
Yanchuk, Serhiy2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: It is a fundamental challenge to understand how the function of a network is related to its structural organization. Adaptive dynamical networks represent a broad class of systems that can change their connectivity over time depending on their dynamical state. The most important feature of such systems is that their function depends on their structure and vice versa. While the properties of static networks have been extensively investigated in the past, the study of adaptive networks is much more challenging. Moreover, adaptive dynamical networks are of tremendous importance for various application fields, in particular, for the models for neuronal synaptic plasticity, adaptive networks in chemical, epidemic, biological, transport, and social systems, to name a few. In this review, we provide a detailed description of adaptive dynamical networks, show their applications in various areas of research, highlight their dynamical features and describe the arising dynamical phenomena, and give an overview of the available mathematical methods developed for understanding adaptive dynamical networks.

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Sprache(n): eng - Englisch
 Datum: 2023-09-042023-08-10
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.physrep.2023.08.001
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Art des Abschluß: -

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Titel: Physics Reports
Genre der Quelle: Zeitschrift, SCI, Scopus
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 1031 Artikelnummer: - Start- / Endseite: 1 - 59 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/physics-reports
Publisher: Elsevier