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      https://www.ias.ac.in/article/fulltext/pram/088/02/0037

    • Keywords

       

      Chaotic time series; complex networks; random graphs.

    • Abstract

       

      Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability densityvariations of the representative attractor from which it is constructed. Here we numerically investigate the properties of recurrence networks from standard low-dimensional chaotic attractors using some basic network measuresand show how the recurrence networks are different from random and scale-free networks. In particular, we show that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise tothe time series and into the classical random graphs by increasing the range of interaction to the system size. We also highlight the effectiveness of a combined plot of characteristic path length and clustering coefficient in capturing the small changes in the network characteristics.

    • Author Affiliations

       

      RINKU JACOB1 K P HARIKRISHNAN1 R MISRA2 G AMBIKA3

      1. Department of Physics, The Cochin College, Cochin 682 002, India
      2. Inter-University Centre for Astronomy and Astrophysics, Ganeshkhind, Pune 411 007, India
      3. Indian Institute of Science Education and Research, Dr Homi Bhabha Road, Pashan, Pune 411 008, India
    • Dates

       
  • Pramana – Journal of Physics | News

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      Posted on July 25, 2019

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