Journal ArticleDOI
Recurrence network modeling and analysis of spatial data.
TLDR
Experimental results show that the generalized recurrence network approach yields superior performance in the visualization of recurrence patterns in spatial data and in the extraction of salient features to characterize recurrence dynamics in spatial systems.Abstract:
Nonlinear dynamical systems exhibit complex recurrence behaviors. Recurrence plot is widely used to graphically represent the patterns of recurrence dynamics and further facilitates the quantification of recurrence patterns, namely, recurrence quantification analysis. However, traditional recurrence methods tend to be limited in their ability to handle spatial data due to high dimensionality and geometric characteristics. Prior efforts have been made to generalize the recurrence plot to a four-dimensional space for spatial data analysis, but this framework can only provide graphical visualization of recurrence patterns in the projected reduced-dimension space (i.e., two- or three- dimensions). In this paper, we propose a new weighted recurrence network approach for spatial data analysis. A weighted network model is introduced to represent the recurrence patterns in spatial data, which account for both pixel intensities and spatial distance simultaneously. Note that each network node represents a location in the high-dimensional spatial data. Network edges and weights preserve complex spatial structures and recurrence patterns. Network representation is shown to be an effective means to provide a complete picture of recurrence patterns in the spatial data. Furthermore, we leverage network statistics to characterize and quantify recurrence properties and features in the spatial data. Experimental results in both simulation and real-world case studies show that the generalized recurrence network approach yields superior performance in the visualization of recurrence patterns in spatial data and in the extraction of salient features to characterize recurrence dynamics in spatial systems.read more
Citations
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Journal ArticleDOI
Development of the method for rapid detection of hazardous atmospheric pollution of cities with the help of recurrence measures
TL;DR: In this article, a method for rapid detection of hazardous pollution of the atmosphere of cities, which is based on dynamic measures of recurrence (repeatability) of the states of the pollution concentration vector, was developed.
Journal ArticleDOI
Introduction to focus issue: Recurrence quantification analysis for understanding complex systems
Journal ArticleDOI
Heterogeneous Recurrence Analysis of Disease-Altered Spatiotemporal Patterns in Multi-Channel Cardiac Signals
TL;DR: The proposed method of heterogeneous recurrence analysis shows strong potential to be further extended for the analysis of other physiological signals such as electroencephalogram (EEG) and electromyography (EMG) signals towards medical decision making.
Journal ArticleDOI
Recurrence network analysis of design-quality interactions in additive manufacturing
TL;DR: In this article, a generalized recurrence network (GRN) was developed to represent the AM spatial image data and quantifiers, namely degree, betweenness, pagerank, closeness, and eigenvector centralities, were extracted to characterize the quality of layerwise builds.
Journal ArticleDOI
Trends in recurrence analysis of dynamical systems
Norbert Marwan,Hauke Kraemer +1 more
TL;DR: The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot-based data analysis and to widen its application potential as discussed by the authors , such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection.
References
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Recurrence plots for the analysis of complex systems
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Journal ArticleDOI
A measure of betweenness centrality based on random walks
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