2020
DOI: 10.1016/j.physa.2020.125108
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Identification and prediction of bifurcation tipping points using complex networks based on quasi-isometric mapping

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Cited by 13 publications
(6 citation statements)
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“…In the context of time series analysis, visibility graphs are part of a family of algorithms based on converting time series to complex networks. Visibility graph algorithms are useful for time series analysis because they can help to identify patterns and trends that would otherwise be difficult to see using traditional non-linear time series analysis and were effectively used to detect chaotic behavior [17] , [18] , bifurcations [19] , phase transitions [20] , and self-organization [21] and fractal behavior [22] . Visibility graphs can be used to study time reversibility in non-stationary systems, as well [23] , and are invariant under affine transformations, such as re-scaling of axes, are always connected, and two consecutive observations of the time series are always connected in the visibility graph.…”
Section: Related Theorymentioning
confidence: 99%
“…In the context of time series analysis, visibility graphs are part of a family of algorithms based on converting time series to complex networks. Visibility graph algorithms are useful for time series analysis because they can help to identify patterns and trends that would otherwise be difficult to see using traditional non-linear time series analysis and were effectively used to detect chaotic behavior [17] , [18] , bifurcations [19] , phase transitions [20] , and self-organization [21] and fractal behavior [22] . Visibility graphs can be used to study time reversibility in non-stationary systems, as well [23] , and are invariant under affine transformations, such as re-scaling of axes, are always connected, and two consecutive observations of the time series are always connected in the visibility graph.…”
Section: Related Theorymentioning
confidence: 99%
“…Modern diagnostic methods assessing the degree of degradation of the mechanical properties of materials have various technical applications [ 17 , 18 ]. In [ 19 ], the identification and forecasting of the turning points of bifurcations using complex networks is presented. In [ 20 ], the results of the identification of complex interactions between biological objects and their modeling in the form of graphs are presented.…”
Section: Relative Workmentioning
confidence: 99%
“…Interest in identifying the state of a material under load has manifested itself in the development of similar issues in related fields of science and technology. The identification of bifurcations in distributed complex networks is considered in [ 3 ]. The identification of the operability of power systems is presented in [ 4 ].…”
Section: Introductionmentioning
confidence: 99%