Associated network family of the unified piecewise linear chaotic family and their relevance
Haoying Niu(牛浩瀛)1 and Jie Liu(刘杰)1,2,†
1 Research Center of Nonlinear Science, Wuhan Textile University, Wuhan 430073, China; 2 School of Mathematics and Physics Science, Wuhan Textile University, Wuhan 430073, China
Abstract Duality analysis of time series and complex networks has been a frontier topic during the last several decades. According to some recent approaches in this direction, the intrinsic dynamics of typical nonlinear systems can be better characterized by considering the related nonlinear time series from the perspective of networks science. In this paper, the associated network family of the unified piecewise-linear (PWL) chaotic family, which can bridge the gap of the PWL chaotic Lorenz system and the PWL chaotic Chen system, was firstly constructed and analyzed. We constructed the associated network family via the original and the modified frequency-degree mapping strategy, as well as the classical visibility graph and horizontal visibility graph strategy, after removing the transient states. Typical related network characteristics, including the network fractal dimension, of the associated network family, are computed with changes of single key parameter . These characteristic vectors of the network are also compared with the largest Lyapunov exponent (LLE) vector of the related original dynamical system. It can be found that, some network characteristics are highly correlated with LLE vector of the original nonlinear system, i.e., there is an internal consistency between the largest Lyapunov exponents, some typical associated network characteristics, and the related network fractal dimension index. Numerical results show that the modified frequency-degree mapping strategy can demonstrate highest correlation, which means it can behave better to capture the intrinsic characteristics of the unified PWL chaotic family.
Corresponding Authors:
Jie Liu
E-mail: liujie@wtu.edu.cn
Cite this article:
Haoying Niu(牛浩瀛) and Jie Liu(刘杰) Associated network family of the unified piecewise linear chaotic family and their relevance 2025 Chin. Phys. B 34 040503
[1] Zou Y, Donner R V, Marwan N, Donges J F and Kurths J 2019 Phys. Rep. 787 1 [2] Silva V F, Silva M E, Ribeiro P and Silva F 2021 WIREs Data Min. Knowl. Discov. 11 e1404 [3] Wolf A, Swift J B, Swinney H L and Vastano J A 1985 Physica D 16 285 [4] Grassberger P and Procaccia I 1983 Phys. Rev. Lett. 50 346 [5] Grassberger P and Procaccia I 1983 Phys. Rev. A 28 2591 [6] Zhang J and Small M 2006 Phys. Rev. Lett. 96 238701 [7] Bradley E and Kantz H 2015 Chaos 25 097610 [8] Azizi Hadis and Sulaimany S 2024 IEEE Access 12 93517 [9] Lacasa L, Luque B, Ballesteros F, Luque J and Nuno J C 2008 Proc. Natl. Acad. Sci. USA 105 4972 [10] Luque B, Lacasa L, Ballesteros F and Luque J 2009 Phys. Rev. E 80 046103 [11] Marwan N, Donges J F, Zou Y, Donner R V and Kurths J 2009 Phys. Lett. A 373 4246 [12] Campanharo A S, Sirer M I, Malmgren R D, Ramos F M and Amaral L A N 2011 PloS ONE 6 e23378 [13] Zhou T T, Jin N D, Gao Z K and Luo Y B 2012 Acta Phys. Sin. 61 030506 (in Chinese) [14] Lacasa, L, Nicosia V and Latora V 2015 Sci. Rep. 5 15508 [15] Wen T, Chen H and Cheong K H 2022 Nonlinear Dyn. 110 2979 [16] Donner R V, Small M, Donges J F, et al. 2011 Int. J. Bifurc. Chaos 21 1019 [17] Li X, Yang D, Liu X and Wu X M 2012 IEEE Circ. Syst. Mag. 12 33 [18] Sun X, Small M, Zhao Y and Xue X 2014 Chaos 24 024402 [19] Gao Z K, Hu L D and Jin N D 2013 Chin. Phys. B 22 050507 [20] Walker D M, Correa D C and Small M 2018 Chaos 28 013101 [21] Liu J, Wang H, Xu H, Bao S and Li L 2020 Chinese Control and Decision Conference, August 22-24, 2020, Hefei, China, p. 4868 [22] Wang X, Han X, Chen Z, Bi Q, Guan S and Zou Y 2022 Chaos, Solitons and Fractals 159 112026 [23] Wang S, Li P, Chen G and Bao C 2024 Chaos 34 043145 [24] Lü J H, Chen G R and Chen D Z 1993 Int. J. Bifurcat. Chaos 14 1507- 1537 [25] Liu J, Lu J andWu X 2004 ICARCV 8th Control, Automation, Robotics and Vision Conference, December 6-9, 2004, Kunming, China, p. 1368 [26] Baghious E H and Jarry P 1993 Int. J. Bifurcat. Chaos 3 201 [27] Ozoguz S, Elwakil A S and Kennedy M P 2002 Int. J. Bifurcat. Chaos 12 1627 [28] Aziz-Alaoui M and Chen G 2002 Int. J. Bifurcat. Chaos 12 147 [29] Nabil H and Tayeb H 2024 Chin. Phys. B 33 120503 [30] Zhao P C, Wei H J, Xu Z K, Chen D Y, Xu B B and Wang Y M 2023 Chin. Phys. B 32 090503 [31] Liu T and Xia X 2024 Chin. Phys. L 41 017102 [32] Song C M, Gallos L K, Havlin S and Makse H A 2007 J. Stat. Mech.- Theory. E 2007 P03006
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.