Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4844
PIRA download icon_1.1View/Download Full Text
Title: Complex network from pseudoperiodic time series : topology versus dynamics
Authors: Zhang, J
Small, M
Issue Date: 14-Jun-2006
Source: Physical review letters, 16 June 2006, v. 96, no. 23, 238701, p. 1-4
Abstract: We construct complex networks from pseudoperiodic time series, with each cycle represented by a single node in the network. We investigate the statistical properties of these networks for various time series and find that time series with different dynamics exhibit distinct topological structures. Specifically, noisy periodic signals correspond to random networks, and chaotic time series generate networks that exhibit small world and scale free features. We show that this distinction in topological structure results from the hierarchy of unstable periodic orbits embedded in the chaotic attractor. Standard measures of structure in complex networks can therefore be applied to distinguish different dynamic regimes in time series. Application to human electrocardiograms shows that such statistical properties are able to differentiate between the sinus rhythm cardiograms of healthy volunteers and those of coronary care patients.
Keywords: Chaos theory
Embedded systems
Large scale systems
Spurious signal noise
Statistical methods
Time series analysis
Topology
Publisher: American Physical Society
Journal: Physical review letters 
ISSN: 0031-9007
EISSN: 1079-7114
DOI: 10.1103/PhysRevLett.96.238701
Rights: Physical Review Letters © 2006 The American Physical Society. The Journal's web site is located at http://prl.aps.org/
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Zhang_Complex_Network _Pseudoperiodic.pdf398.72 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

131
Last Week
0
Last month
Citations as of May 15, 2022

Downloads

1,057
Citations as of May 15, 2022

SCOPUSTM   
Citations

598
Last Week
1
Last month
3
Citations as of May 19, 2022

WEB OF SCIENCETM
Citations

519
Last Week
1
Last month
2
Citations as of May 19, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.