Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems

Norbert Marwan, Jurgen Kurths

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)


We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.
Original languageEnglish
Article number097609
Number of pages10
Issue number9
Early online date10 Apr 2015
Publication statusPublished - Sept 2015

Bibliographical note

We would like to acknowledge support from the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP, the DFG project “Investigation of past and present climate dynamics and its stability by means of a spatio-temporal analysis of climate data using complex networks” (MA 4759/4-1), and project “Gradual environmental change versus single catastrophe—Identifying drivers of mammalian evolution” (SAW-2013-IZW-2), funded by the Leibniz Association (WGL). Moreover, we thank Niklas Boers for calculations and helpful comments.


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