Cross over of recurrence networks to random graphs and random geometric graphs

详细信息    查看全文

• 作者：RINKU JACOB ; K P HARIKRISHNAN ; R MISRA ; G AMBIKA
• 关键词：Chaotic time series ; complex networks ; random graphs.
• 刊名：Pramana
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：88
• 期：2
• 全文大小：
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics, general; Astronomy, Observations and Techniques; Astrophysics and Astroparticles;
• 出版者：Springer India
• ISSN：0973-7111
• 卷排序：88

文摘

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 density variations 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 measures and 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 to the 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.