Power-laws in recurrence networks from dynamical systems

Y. Zou, J. Heitzig, R. V. Donner, J. F. Donges, J. D. Farmer, R. Meucci, S. Euzzor, N. Marwan, J. Kurths

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


Recurrence networks are a novel tool of nonlinear time series analysis allowing the characterisation of higher-order geometric properties of complex dynamical systems based on recurrences in phase space, which are a fundamental concept in classical mechanics. In this letter, we demonstrate that recurrence networks obtained from various deterministic model systems as well as experimental data naturally display power-law degree distributions with scaling exponents gamma that can be derived exclusively from the systems' invariant densities. For one-dimensional maps, we show analytically that gamma is not related to the fractal dimension. For continuous systems, we find two distinct types of behaviour: power-laws with an exponent gamma depending on a suitable notion of local dimension, and such with fixed gamma=1. Copyright (C) EPLA, 2012

Original languageEnglish
Article number48001
Number of pages6
JournalEurophysics Letters
Issue number4
Publication statusPublished - May 2012


  • pseudoperiodic time-series
  • complex networks
  • strange attractors
  • chaos
  • behavior


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