Complex network approach to characterize the statistical features of the sunspot series

Y. Zou, Michael Small, Z. Liu, J. Kurths

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    46 Citations (Scopus)


    Complex network approaches have been recently developed as an alternative framework to study the statistical features of time-series data. We perform a visibility-graph analysis on both the daily and monthly sunspot series. Based on the data, we propose two ways to construct the network: one is from the original observable measurements and the other is from a negative-inverse- transformed series. The degree distribution of the derived networks for the strong maxima has clear non-Gaussian properties, while the degree distribution for minima is bimodal. The long-term variation of the cycles is reflected by hubs in the network that span relatively large time intervals. Based on standard network structural measures, we propose to characterize the long-term correlations by waiting times between two subsequent events. The persistence range of the solar cycles has been identified over 15-1000 days by a power-law regime with scaling exponent γ = 2.04 of the occurrence time of two subsequent strong minima. In contrast, a persistent trend is not present in the maximal numbers, although maxima do have significant deviations from an exponential form. Our results suggest some new insights for evaluating existing models. © 2014 IOP Publishing and Deutsche Physikalische Gesellschaft.
    Original languageEnglish
    Article number013051
    Number of pages10
    JournalNew Journal of Physics
    Publication statusPublished - 30 Jan 2014


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