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We introduce a new method of characterizing the seismic complex systems using a procedure of transformation from complex networks into time series. The undirected complex network is constructed from seismic hypocenters data. Network nodes are marked by their connectivity. The walk on the graph following the time of succeeding seismic events generates the connectivity time series which contains, both the space and time, features of seismic processes. This procedure was applied to four seismic data sets registered in Chile. It was shown that multifractality of constructed connectivity time series changes due to the particular geophysics characteristics of the seismic zones—it decreases with the occurrence of large earthquakes—and shows the spatiotemporal organization of these seismic systems. [ABSTRACT FROM AUTHOR]

A complex network analysis of the seismic activity in the central zone of Chile is made, where each node corresponds to a location, where a seism occurs. The $$M_w =$$ 8.3 Illapel earthquake (16 September 2015) is included in the data set studied. Assuming a self-similar data network, the value of the power law characteristic exponent $$\gamma$$ for the link probability distribution of the directed network and the value of the power law characteristic exponent $$\delta$$ for the cumulative distribution of the betweenness centrality are studied, before and after the earthquake. Both exponents have a different values before and after the earthquake when the network is built with cell size of 5 $$\times$$ 5 $$\times$$ 5 km, but there is no difference when the cell size is 10 $$\times$$ 10 $$\times$$ 10 km. The exponents were evaluated for the data set with the total number of seismic events and for three cutoffs in magnitude. There is not much variation when applying the cutoff. Variations of both exponents are found when both subsets, before and after the main event, are compared, suggesting that the topological features of the complex network of seisms are modified by major events. [ABSTRACT FROM AUTHOR]