The monthly CLUe training is part of the Complexity Laboratorium Utrecht (CLUe) activities. The aim of the CLUe training is to provide hands-on tutorials on particular Complexity Science software/packages/numerical methods, which will be beneficial to your current or future studies.
On Friday 15 September 2017 from 12:00 to 13:00, Dr. Qingyi Feng (researcher from the CLUe) will show you how to use the python package pyunicorn for recurrence network analysis and also domenstrate how to use the CLUe server.
A brief introduction to pyunicorn
pyunicorn (Unified Complex Network and RecurreNce analysis toolbox) is a fully object-oriented Python package for for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neurosciences representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. (Source)
Simple examples of recurrence network analysis
Recurrence network analysis is a novel paradigm for nonlinear time series analysis. Starting from the concept of recurrences in phase space, the recurrence matrix of a time series is interpreted as the adjacency matrix of an associated complex network, which links different points in time if the considered states are closely neighboured in phase space. In comparison with similar network-based techniques the new approach has important conceptual advantages, and can be considered as a unifying framework for transforming time series into complex networks that also includes other existing methods as special cases. It has been demonstrated here that there are fundamental relationships between many topological properties of recurrence networks and different nontrivial statistical properties of the phase space density of the underlying dynamical system. Hence, this novel interpretation of the recurrence matrix yields new quantitative characteristics (such as average path length, clustering coefficient, or centrality measures of the recurrence network) related to the dynamical complexity of a time series, most of which are not yet provided by other existing methods of nonlinear time series analysis. (Source)