Complex network from pseudoperiodic time series: topology versus dynamics.

By Jie Zhang, Michael Small. Physical Review Letters, 2006

693 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/df4f574879df9f8b5b3981d7c9a45ee3ddaa2299

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Yong Zou
2. Norbert Marwan
3. Jonathan F. Donges
4. Reik V. Donner
5. Jürgen Kurths
6. Wei‐Xing Zhou
7. W. Yuan
8. Chuang Liu

In Economics and Econometrics

1. Norbert Marwan
2. Jürgen Kurths
3. Yong Zou
4. Reik V. Donner
5. Jonathan F. Donges
6. Wei‐Xing Zhou
7. Minggang Wang
8. W. Yuan

In Statistical and Nonlinear Physics

1. Norbert Marwan
2. Yong Zou
3. Reik V. Donner
4. Jonathan F. Donges
5. Jürgen Kurths
6. Wei‐Xing Zhou
7. W. Yuan
8. Chuang Liu

Citations in the corpus

• Superfamily phenomena and motifs of networks induced from time series (2008, background): “Zhang and Small (1) first introduced a transformation from pseudoperiodic (that is, oscillatory) time series to complex networks.”
• The role of fluctuating modes of autocorrelation in crude oil prices (2014, methodology): “introduced a method to address pseudoperiodic time series and found that the structure of the corresponding network depended on the dynamics of the series [4,5].”
• Exact results of the limited penetrable horizontal visibility graph associated to random time series and its application (2017, background): “With the development of complex network theories [4–7], a new multidisciplinary methodology for characterizing nonlinear time series using complex network science has emerged and rapidly expanded [8–24].”
• Complex network approach for recurrence analysis of time series (2009, positive): “ctually not necessary to consider the phase space trajectory as a network. Recently, the very first steps in the direction of bridging complex network theory and recurrence analysis have been reported [31, 32]. In these works, the local properties of phase space trajectories have been studied using c”
• Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence (2009, methodology): “introduced a method to deal with the pseudoperiodic time series and found that th e structure of the corresponding network depended on the dynamics of the series [8, 9].”
• Recurrence networks—a novel paradigm for nonlinear time series analysis (2009, background): “A generalization of the cycle networks method used by Zhang and Small that can also be applied to time series without obvious oscillatory components has been suggested by Yang and Yang [37] using a simple embedding of an arbitrary time series.”
• A Survey of Methods for Time Series Change Point Detection (2016, neutral): “This graph can be defined based on a minimum spanning tree [26], minimum distance pairing [52], nearest neighbor graph [26,52], or visibility graph [40,66].”