Literature DB >> 23767578

Horizontal visibility graphs generated by type-I intermittency.

Ángel M Núñez1, Bartolo Luque, Lucas Lacasa, Jose Patricio Gómez, Alberto Robledo.   

Abstract

The type-I intermittency route to (or out of) chaos is investigated within the horizontal visibility (HV) graph theory. For that purpose, we address the trajectories generated by unimodal maps close to an inverse tangent bifurcation and construct their associated HV graphs. We show how the alternation of laminar episodes and chaotic bursts imprints a fingerprint in the resulting graph structure. Accordingly, we derive a phenomenological theory that predicts quantitative values for several network parameters. In particular, we predict that the characteristic power-law scaling of the mean length of laminar trend sizes is fully inherited by the variance of the graph degree distribution, in good agreement with the numerics. We also report numerical evidence on how the characteristic power-law scaling of the Lyapunov exponent as a function of the distance to the tangent bifurcation is inherited in the graph by an analogous scaling of block entropy functionals defined on the graph. Furthermore, we are able to recast the full set of HV graphs generated by intermittent dynamics into a renormalization-group framework, where the fixed points of its graph-theoretical renormalization-group flow account for the different types of dynamics. We also establish that the nontrivial fixed point of this flow coincides with the tangency condition and that the corresponding invariant graph exhibits extremal entropic properties.

Mesh:

Year:  2013        PMID: 23767578     DOI: 10.1103/PhysRevE.87.052801

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  4 in total

1.  Visibility Graph Based Time Series Analysis.

Authors:  Mutua Stephen; Changgui Gu; Huijie Yang
Journal:  PLoS One       Date:  2015-11-16       Impact factor: 3.240

2.  Type-I intermittency from Markov binary block visibility graph perspective.

Authors:  Pejman Bordbar; Sodeif Ahadpour
Journal:  J Appl Stat       Date:  2020-05-12       Impact factor: 1.416

3.  Network structure of multivariate time series.

Authors:  Lucas Lacasa; Vincenzo Nicosia; Vito Latora
Journal:  Sci Rep       Date:  2015-10-21       Impact factor: 4.379

4.  A combinatorial framework to quantify peak/pit asymmetries in complex dynamics.

Authors:  Uri Hasson; Jacopo Iacovacci; Ben Davis; Ryan Flanagan; Enzo Tagliazucchi; Helmut Laufs; Lucas Lacasa
Journal:  Sci Rep       Date:  2018-02-23       Impact factor: 4.379

  4 in total

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