English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 PreviousNext  
  Multiplex recurrence networks

Eroglu, D., Marwan, N., Stebich, M., Kurths, J. (2018): Multiplex recurrence networks. - Physical Review E, 97, 012312.
https://doi.org/10.1103/PhysRevE.97.012312

Item is

Files

show Files
hide Files
:
8082.pdf (Publisher version), 3MB
 
File Permalink:
-
Name:
8082.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Eroglu, Deniz1, Author              
Marwan, Norbert1, Author              
Stebich, M.2, Author
Kurths, Jürgen1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: We have introduced a multiplex recurrence network 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 similarly when one environmental factor acts as the dominant driving force.

Details

show
hide
Language(s):
 Dates: 2018
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevE.97.012312
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 8082
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Paleoclimate
Research topic keyword: Climate impacts
Research topic keyword: Ecosystems
Research topic keyword: Complex Networks
Model / method: Nonlinear Data Analysis
Regional keyword: Asia
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Physical Review E
Source Genre: Journal, SCI, Scopus, p3
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 97 Sequence Number: 012312 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150218