Abstract
Recently, several complex network approaches to time series analysis have been developed and applied to study a wide range of model systems as well as real-world data, e.g., geophysical or financial time series. Among these techniques, recurrence-based concepts and prominently ε-recurrence networks, most faithfully represent the geometrical fine structure of the attractors underlying chaotic (and less interestingly non-chaotic) time series. In this paper we demonstrate that the well known graph theoretical properties local clustering coefficient and global (network) transitivity can meaningfully be exploited to define two new local and two new global measures of dimension in phase space: local upper and lower clustering dimension as well as global upper and lower transitivity dimension. Rigorous analytical as well as numerical results for self-similar sets and simple chaotic model systems suggest that these measures are well-behaved in most non-pathological situations and that they can be estimated reasonably well using ε-recurrence networks constructed from relatively short time series. Moreover, we study the relationship between clustering and transitivity dimensions on the one hand, and traditional measures like pointwise dimension or local Lyapunov dimension on the other hand. We also provide further evidence that the local clustering coefficients, or equivalently the local clustering dimensions, are useful for identifying unstable periodic orbits and other dynamically invariant objects from time series. Our results demonstrate that ε-recurrence networks exhibit an important link between dynamical systems and graph theory.
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References
N. Marwan, M.C. Romano, M. Thiel, J. Kurths, Phys. Rep. 438, 237 (2007)
J.P. Eckmann, S.O. Kamphorst, D. Ruelle, Europhys. Lett. 4, 973 (1987)
N. Marwan, Eur. Phys. J. ST 164, 3 (2008)
H. Poincaré, Acta Mathematica 13, A3 (1890)
M. Thiel, M.C. Romano, J. Kurths, Phys. Lett. A 330, 343 (2004)
Y. Hirata, S. Horai, K. Aihara, Eur. Phys. J. ST 164, 13 (2008)
G. Robinson, M. Thiel, Chaos 19, 023104 (2009)
M. Thiel, M.C. Romano, P.L. Read, J. Kurths, Chaos 14, 234 (2004)
R. Albert, A.L. Barabasi, Rev. Mod. Phys. 74, 47 (2002)
M.E.J. Newman, SIAM Rev. 45, 167 (2003)
S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, D.U. Hwang, Phys. Rep. 424, 175 (2006)
L.D.F. Costa, F.A. Rodrigues, G. Travieso, P.R.V. Boas, Adv. Phys. 56, 167 (2007)
N. Marwan, J.F. Donges, Y. Zou, R.V. Donner, J. Kurths, Phys. Lett. A 373, 4246 (2009)
Z. Gao, N. Jin, Phys. Rev. E 79, 066303 (2009a)
Z. Gao, N. Jin, Chaos 19, 033137 (2009b)
R.V. Donner, Y. Zou, J.F. Donges, N. Marwan, J. Kurths, Phys. Rev. E 81, R015101 (2010)
R.V. Donner, Y. Zou, J.F. Donges, N. Marwan, J. Kurths, New J. Phys. 12, 033025 (2010)
R.V. Donner, M. Small, J.F. Donges, N. Marwan, Y. Zou, R. Xiang, J. Kurths, Int. J. Bifurc. Chaos (in press), arXiv:1010.6032
M. Penrose, Random Geometric Graphs (Oxford University Press, Oxford, 2003)
S. Felsner, Geometric Graphs and Arrangements, 3rd edn. (Vieweg, Wiesbaden, 2004)
C. Herrmann, M. Barthélemy, P. Provero, Phys. Rev. E 68, 026128 (2003)
M.A. Carreira-Perpiñan, R.S. Zemel, Proximity graphs for clustering and manifold learning, in Advances in Neural Information Processing Systems 17 (NIPS 2004), edited by L.K. Saul, Y. Weiss, L. Bottou (MIT Press, Cambridge, 2005), pp. 225–232
C. Zhou, L. Zemanova, G. Zamora, C.C. Hilgetag, J. Kurths, Phys. Rev. Lett. 97, 238103 (2006)
C. Zhou, L. Zemanova, G. Zamora-Lopez, C.C. Hilgetag, J. Kurths, New J. Phys. 9, 178 (2007)
J.F. Donges, Y. Zou, N. Marwan, J. Kurths, Eur. Phys. J. ST 174, 157 (2009)
J.F. Donges, Y. Zou, N. Marwan, J. Kurths, Europhys. Lett. 87, 48007 (2009)
I. Borg, P. Groenen, Modern Multidimensional Scaling: theory and applications, 2nd edn. (Springer, New York, 2005)
J.B. Tenenbaum, V. de Silva, J.C. Langford, Science 290, 2319 (2000)
M. Dellnitz, M. Hessel-von Molo, P. Metzner, R. Preis, C. Schütte, in Analysis, Modeling and Simulation of Multiscale Problems, edited by A. Mielke (Springer, Heidelberg, 2006), pp. 619–646
K. Padberg, B. Thiere, R. Preis, M. Dellnitz, Communications in Nonlinear Science and Numerical Simulation 14, 4176 (2009)
G. Nicolis, A. García Cantú, C. Nicolis, Int. J. Bifurc. Chaos 15, 3467 (2005)
J. Zhang, M. Small, Phys. Rev. Lett. 96, 238701 (2006)
Y. Yang, H. Yang, Physica A 387, 1381 (2008)
L. Lacasa, B. Luque, F. Ballesteros, J. Luque, J.C. Nuno, Proceedings of the National Academy of Sciences USA 105, 4972 (2008)
Y. Shimada, T. Kimura, T. Ikeguchi, Analysis of Chaotic Dynamics Using Measures of the Complex Network Theory, in Artificial Neural Networks - ICANN 2008, Pt. I, edited by V. Kurkova, R. Neruda, J. Koutnik Lecture Notes in Computer Science (Springer, New York, 2008), Vol. 5163, pp. 61–70
X. Xu, J. Zhang, M. Small, Proceedings of the National Academy of Sciences USA 105, 19601 (2008)
Y. Zou, R.V. Donner, J.F. Donges, N. Marwan, J. Kurths, Chaos 20, 043130 (2010)
A. Arenas, A. Diaz-Guilera, J. Kurths, Y. Moreno, C. Zhou, Phys. Rep. 469, 93 (2008)
S.V. Buldyrev, R. Parshani, G. Paul, H.E. Stanley, S. Havlin, Nature 464, 1025 (2010)
J.C. Oxtoby, Proceedings of the National Academy of Sciences USA 23, 443 (1937)
A. Katok, B. Hasselblatt, Introduction to the modern theory of dynamical systems (Cambridge University Press, Cambridge, 1995)
D.J. Watts, S.H. Strogatz, Nature 393, 440 (1998)
A. Barrat, M. Weigt, Eur. Phys. J. B 13, 547 (2000)
M.E.J. Newman, Phys. Rev. E 64, 016131 (2001)
S.N. Dorogovtsev, A.V. Goltsev, J.F.F. Mendes, Phys. Rev. E 65, 066122 (2002)
G. Szabó, M. Alava, J. Kertész, Phys. Rev. E 67, 056102 (2003)
E. Ravasz, A.L. Somera, D.A. Mongru, Z.N. Oltvai, A.L. Barabasi, Science 297, 1551 (2002)
E. Ravasz, A.L. Barabási, Phys. Rev. E 67, 026112 (2003)
A. Vázquez, Phys. Rev. E 67, 056104 (2003)
P. Grassberger, Phys. Lett. A 97, 227 (1983)
P. Grassberger, I. Procaccia, Phys. Rev. Lett. 50, 346 (1983)
J.G. Reid, T.A. Trainor (2003), arXiv:math-ph/0305022
J.D. Farmer, E. Ott, J.A. Yorke, Physica D 7, 153 (1983)
J. Kaplan, J. Yorke, in Functional Differential Equations and Approximation of Fixed Points, edited by H.O. Peitgen, H.O. Walther, Lecture Notes in Mathematics (Springer Berlin/Heidelberg, 1979), Vol. 730, pp. 204–227
E. Ott, Chaos in Dynamical Systems, 2nd edn. (Cambridge University Press, Cambridge, 2002)
B.R. Hunt, Nonlinearity 9, 845 (1996)
K. Gelfert, Journal for Analysis and its Applications 22, 553 (2003)
Y. Zou, J. Heitzig, J.D. Farmer, R. Meucci, S. Euzzor, N. Marwan, R.V. Donner, J.F. Donges, J. Kurths (in prep.)
L. Lacasa, B. Luque, J. Luque, J.C. Nuno, Europhys. Lett. 86, 30001 (2009)
X.H. Ni, Z.Q. Jiang, W.X. Zhou, Phys. Lett. A 373, 3822 (2009)
J. Dall, M. Christensen, Phys. Rev. E 66, 016121 (2002)
J.C. Sprott, Chaos and Time-Series Analysis (Oxford University Press, Oxford, 2003)
J. Heitzig, J.F. Donges, Y. Zou, N. Marwan, J. Kurths (2011), arXiv:1101.4757 [physics.data-an]
E.M. Oblow, Phys. Lett. A 128, 406 (1988)
N. Marwan, N. Wessel, U. Meyerfeldt, A. Schirdewan, J. Kurths, Phys. Rev. E 66, 026702 (2002)
R.V. Donner, J.F. Donges, Y. Zou, N. Marwan, J. Kurths, Proc. NOLTA 2010 (2010), pp. 87–90
A. Veronig, M. Messerotti, A. Hanslmeier, A&A 357, 337 (2000)
S. Gratrix, J.N. Elgin, Phys. Rev. Lett. 92, 014101 (2004)
P. Grassberger, I. Procaccia, Physica D 9, 189 (1983)
D.P. Lathrop, E.J. Kostelich, Phys. Rev. A 40, 4028 (1989)
C. Grebogi, E. Ott, J.A. Yorke, Phys. Rev. A 37, 1711 (1988)
M. Hénon, Commun. Math. Phys. 50, 69 (1976)
P. Cvitanović, G.H. Gunaratne, I. Procaccia, Phys. Rev. A 38, 1503 (1988)
J.A.C. Gallas, Phys. Rev. Lett. 70, 2714 (1993)
J.A.C. Gallas, Physica A 202, 196 (1994)
M. Thiel, Ph.D. thesis, University of Potsdam, 2004
C. Bonatto, J.A.C. Gallas, Phil. Trans. R. Soc. A 366, 505 (2008)
J.A.C. Gallas, Int. J. Bifurc. Chaos 20, 197 (2010)
Y. Saiki, Nonlinear Processes in Geophysics 14, 615 (2007)
G. Csárdi, T. Nepusz, InterJournal CX.18, 1695 (2006)
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Donner, R.V., Heitzig, J., Donges, J.F. et al. The geometry of chaotic dynamics — a complex network perspective. Eur. Phys. J. B 84, 653–672 (2011). https://doi.org/10.1140/epjb/e2011-10899-1
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DOI: https://doi.org/10.1140/epjb/e2011-10899-1


