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https://doi.org/10.1103/physre...
Article . 2018 . Peer-reviewed
License: APS Licenses for Journal Article Re-use
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2020
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Multiplex recurrence networks

Authors: Juergen Kurths; Juergen Kurths; Juergen Kurths; Martina Stebich; Norbert Marwan; Deniz Eroglu; Deniz Eroglu;

Multiplex recurrence networks

Abstract

We have introduced a novel multiplex recurrence network (MRN) approach by combining recurrence networks with the multiplex network approach in order to investigate multivariate time series. The potential use of this approach is demonstrated on coupled map lattices and a typical example from palaeobotany research. In both examples, topological changes in the multiplex recurrence networks allow for the detection of regime changes in their dynamics. The method goes beyond classical interpretation of pollen records by considering the vegetation as a whole and using the intrinsic similarity in the dynamics of the different regional vegetation elements. We find that the different vegetation types behave more similar when one environmental factor acts as the dominant driving force.

Countries
United Kingdom, Germany
Keywords

COMPLEX NETWORKS, DYNAMICS, 550, FOS: Physical sciences, Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Nonlinear Sciences - Chaotic Dynamics, STRANGE ATTRACTORS, CLIMATE, PLOTS, VARIABILITY, QUANTIFICATION ANALYSIS, QC Physics, TIME-SERIES ANALYSIS, SYSTEMS, Physics - Data Analysis, Statistics and Probability, HOLOCENE, Chaotic Dynamics (nlin.CD), QC, Data Analysis, Statistics and Probability (physics.data-an)

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    37
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
37
Top 10%
Top 10%
Top 10%
Green