  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 which 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 $\gamma = 2.04$
of the occurrence time of the two subsequent and successive 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. The power-law regime
suggested by the waiting times does indicate that there are some level of
predictable patterns in the minima.
