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General Considerations

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Dynamical Systems on Networks

Part of the book series: Frontiers in Applied Dynamical Systems: Reviews and Tutorials ((FIADS,volume 4))

Abstract

Now that we have discussed several families of models as motivation (and because they are interesting in their own right), we present some general considerations for studying dynamical systems on networks. We alluded to several of these ideas in our prior discussions.

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Notes

  1. 1.

    Note that we previously used the notation \(\lambda\) in Sec. 3.2.1 to represent the transmission rate in the SI model. In this section, we use \(\lambda\) with appropriate subscripts to represent the eigenvalues of A.

  2. 2.

    Although we were able to separate the dependence on structure and dynamics in our example, note that the analysis is more complicated when the equilibrium points are different for different nodes and when considering other types of behavior (e.g., periodic or chaotic dynamics) [244].

  3. 3.

    See [175] for a discussion of tensors.

  4. 4.

    When calculating a sample mean using numerical simulations of a dynamical system on a network (or a family of networks), there are several possible sources of stochasticity: (1) choice of initial condition, (2) choice of which nodes to update (when considering asynchronous updating), (3) the update rule itself, (4) parameter values that are used in an update rule, and (5) selection of particular networks from a random-graph ensemble. Some or all of these sources of randomness can be present when studying dynamical systems on networks, and (when possible) it is also desirable to compare the sample means to ensemble averages (i.e., expectations over a suitable probability distribution).

  5. 5.

    It may also be useful to develop analogous class-based approximations that use structural characteristics other than degree.

References

  1. A. Arenas, A. Díaz-Guilera, J. Kurths, Y. Moreno, C. Zhou, Synchronization in complex networks. Phys. Rep. 469(3), 93–153 (2008)

    Article  MathSciNet  Google Scholar 

  2. M. Barahona, L. Pecora, Synchronization in small world systems. Phys. Rev. Lett. 89(5), 054101 (2002)

    Google Scholar 

  3. V.N. Belykh, I.V. Belykh, M. Hasler, Connection graph stability method for synchronized coupled chaotic systems. Physica D 195(1–2), 159–187 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  4. I.V. Belykh, V.N. Belykh, M. Hasler, Blinking model and synchronization in small-world networks with a time-varying coupling. Physica D 195(1–2), 188–206 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  5. S. Boccaletti, G. Bianconi, R. Criado, C.I. del Genio, J. Gómez-Gardeñes, M. Romance, I. Sendiña-Nadal, Z. Wang, M. Zanin, The structure and dynamics of multilayer networks. Phys. Rep. 544(1), 1–122 (2014)

    Article  MathSciNet  Google Scholar 

  6. M. Boguñá, R. Pastor-Satorras, Epidemic spreading in correlated complex networks. Phys. Rev. E 66(4), 047104 (2002)

    Google Scholar 

  7. G.A. Böhme, T. Gross, Analytical calculation of fragmentation transitions in adaptive networks. Phys. Rev. E 83(3), 35101 (2011)

    Google Scholar 

  8. K.A. Bold, K. Rajendran, B. Ráth, I.G. Kevrekidis, An equation-free approach to coarse-graining the dynamics of networks. J. Comput. Dyn. 1(1), 111–134 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. F. Brauer, C. Castillo-Chavez, Mathematical Models in Population Biology and Epidemiology, 2nd edn. (Springer, New York, 2012)

    Book  MATH  Google Scholar 

  10. L.A. Bunimovich, B.Z. Webb, Isospectral compression and other useful isospectral transformations of dynamical networks. Chaos 22(3), 033118 (2012)

    Google Scholar 

  11. L.A. Bunimovich, B.Z. Webb, Isospectral graph transformations, spectral equivalence, and global stability of dynamical networks. Nonlinearity 25(1), 211–254 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  12. G. Craciun, M. Feinberg, Multiple equilibria in complex chemical reaction networks. I. The injectivity property. SIAM J. Appl. Math. 65(5), 1526–1546 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  13. G. Craciun, M. Feinberg, Multiple equilibria in complex chemical reaction networks. II. The species-reaction graph. SIAM J. Appl. Math. 66(4), 1321–1338 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. G. Demirel, F. Vázquez, G.A. Bhöme, T. Gross, Moment-closure approximations for discrete adaptive networks. Physica D 267(1), 68–80 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. A.-L. Do, S. Boccaletti, T. Gross, Graphical notation reveals topological stability criteria for collective dynamics in complex networks. Phys. Rev. Lett. 108(19), 194102 (2012)

    Google Scholar 

  16. P.S. Dodds, K.D. Harris, C.M. Danforth, Limited imitation contagion on random networks: Chaos, universality, and unpredictability. Phys. Rev. Lett. 110(15), 158701 (2013)

    Google Scholar 

  17. J. Epperlain, A.-L. Do, T. Gross, S. Siegmund, Meso-scale obstructions to stability of 1D center manifolds for networks of coupled differential equations with symmetric Jacobian. Physica D 261(3), 1–7 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  18. K.S. Fink, G. Johnson, T. Carroll, D. Mar, L. Pecora, Three coupled oscillators as a universal probe of synchronization stability in coupled oscillator arrays. Phys. Rev. E 61(5), 5080–5090 (2000)

    Article  Google Scholar 

  19. T. Gedeon, S. Harker, H. Kokubu, K. Mischaikow, H. Ok, Global dynamics for steep sigmoidal nonlinearities in two dimensions (2015). arXiv:1508.02438

    Google Scholar 

  20. J.P. Gleeson, High-accuracy approximation of binary-state dynamics on networks. Phys. Rev. Lett. 107(6), 068701 (2011)

    Google Scholar 

  21. J.P. Gleeson, Binary-state dynamics on complex networks: Pair approximation and beyond. Phys. Rev. X 3(2), 021004 (2013)

    Google Scholar 

  22. J.P. Gleeson, S. Melnik, J.A. Ward, M.A. Porter, P.J. Mucha, Accuracy of mean-field theory for dynamics on real-world networks. Phys. Rev. E 85(2), 026106 (2012)

    Google Scholar 

  23. M. Golubitsky, R. Lauterbach, Bifurcations from synchrony in homogeneous networks: Linear theory. SIAM J. Appl. Dyn. Syst. 8(1), 40–75 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  24. M. Golubitsky, I. Stewart, Patterns of synchrony in coupled cell networks with multiple arrows. SIAM J. Appl. Dyn. Syst. 4(1), 78–100 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  25. E. Gross, H.A. Harrington, Z. Rosen, B. Sturmfels, Algebraic systems biology: A case study for the Wnt pathway (2015). arXiv:1502.03188

    Google Scholar 

  26. J. Guckenheimer, P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields. Number 42 in Applied Mathematical Sciences (Springer, New York, 1983)

    Google Scholar 

  27. H.A. Harrington, K.L. Ho, T. Thorne, M.P.H. Stumpf, Parameter-free model discrimination criterion based on steady-state coplanarity. Proc. Natl. Acad. Sci. U. S. A. 109(39), 15746–15751 (2012)

    Article  Google Scholar 

  28. T. House, Algebraic moment closure for population dynamics on discrete structures. Bull. Math. Biol. 77(4), 646–659 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  29. T. Ichinomiya, Frequency synchronization in a random oscillator network. Phys. Rev. E 70(2), 026116 (2004)

    Google Scholar 

  30. B. Joshi, A. Shiu, A survey of methods for deciding whether a reaction network is multistationary. Math. Model. Nat. Phenom. 10(5), 47–67 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  31. M. Kivelä, A. Arenas, M. Barthelemy, J.P. Gleeson, Y. Moreno, M.A. Porter. Multilayer networks. J. Complex Networks 2(3), 203–271 (2014)

    Article  Google Scholar 

  32. T.G. Kolda, B.W. Bader, Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  33. C. Kuehn, Moment closure — A brief review (2015). arXiv:1505.02190,

    Google Scholar 

  34. J. Li, W.-X. Wang, Y.-C. Lai, C. Grebogi, Reconstructing complex networks with binary-state dynamics (2015). arXiv:1511.06852

    Google Scholar 

  35. J. Lindquist, J. Ma, P. van den Driessche, F.H. Willeboordse, Effective degree network disease models. J. Math. Biol. 62(2), 143–164 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  36. A.L. MacLean, Z. Rosen, H. Byrne, H.A. Harrington, Parameter-free methods distinguish Wnt pathway models and guide design of experiments. Proc. Natl. Acad. Sci. U.S.A. 112(9), 2652–2657 (2015)

    Article  Google Scholar 

  37. V. Marceau, P.-A. Noël, L. Hébert-Dufresne, A. Allard, L.J. Dubé, Adaptive networks: Coevolution of disease and topology. Phys. Rev. E 82(3), 036116 (2010)

    Google Scholar 

  38. G.S. Medvedev, The nonlinear heat equation on dense graphs and graph limits. SIAM J. Math. Anal. 46(4), 2743–2766 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  39. G.S. Medvedev, X. Tang, Stability of twisted states in the Kuramoto model on Cayley and random graphs. J. Nonlinear Sci. 25(6), 1169–1208 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  40. D. Mehta, N. Daleo, F. Dörfler, J.D. Hauenstein, Algebraic geometrization of the Kuramoto model: Equilibria and stability analysis (2014). arXiv:1412.0666

    Google Scholar 

  41. S. Melnik, J.A. Ward, J.P. Gleeson, M.A. Porter, Multi-stage complex contagions. Chaos 23(1), 013124 (2013)

    Google Scholar 

  42. J.C. Miller, A note on a paper by Erik Volz: SIR dynamics in random networks. J. Math. Biol. 62(3), 349–358 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  43. J.C. Miller, I.Z. Kiss, Epidemic spread in networks: Existing methods and current challenges. Math. Modell. Nat. Phenom. 9(2), 4–42, 1 (2014)

    Google Scholar 

  44. M.E.J. Newman, Networks: An Introduction (Oxford University Press, Oxford, 2010)

    Book  MATH  Google Scholar 

  45. T. Nishikawa, A.E. Motter, Y.-C. Lai, F.C. Hoppensteadt, Heterogeneity in oscillator networks: Are smaller worlds easier to synchronize? Phys. Rev. Lett. 91(1), 014101 (2003)

    Google Scholar 

  46. R. Pastor-Satorras, A. Vespignani, Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86(14), 3200 (2001)

    Google Scholar 

  47. R. Pastor-Satorras, C. Castellano, P. Van Mieghem, A. Vespignani, Epidemic processes in complex networks. Rev. Mod. Phys. 87(4), 925–979 (2015)

    Article  MathSciNet  Google Scholar 

  48. L.M. Pecora, T.L. Carroll, Master stability functions for synchronized coupled systems. Phys. Rev. Lett. 80(10), 2109–2112 (1998)

    Article  Google Scholar 

  49. L.M. Pecora, T.L. Carroll, Master stability function for globally synchronized systems, in Encyclopedia of Computational Neuroscience, ed. by D. Jaeger, R. Jung (Springer, New York, 2014), pp. 1–13

    Chapter  Google Scholar 

  50. L.M. Pecora, T.L. Caroll, Synchronization of chaotic systems. Chaos 25(9), 097611 (2015)

    Google Scholar 

  51. M.A. Porter, Small-world network. Scholarpedia 7(2), 1739 (2012)

    Google Scholar 

  52. G.X. Qi, H.B. Huang, C.K. Shen, H.J. Wang, L. Chen, Predicting the synchronization time in coupled-map networks. Phys. Rev. E 77(5), 056205 (2008)

    Google Scholar 

  53. K. Rajendran, I.G. Kevrekidis, Coarse graining the dynamics of heterogeneous oscillators in networks with spectral gaps. Phys. Rev. E 84(3), 036708 (2011)

    Google Scholar 

  54. A. Solé-Ribalta, M. De Domenico, N.E. Kouvaris, A. Díaz-Guilera, S.Gómez, and A. Arenas. Spectral properties of the Laplacian of multiplex networks. Phys. Rev. E 88(3), 032807 (2013)

    Google Scholar 

  55. I. Stewart, M. Golubitsky, M. Pivato, Symmetry groupoids and patterns of synchrony in coupled cell networks. SIAM J. Appl. Dyn. Syst. 2(4), 609–646 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  56. S.H. Strogatz, Nonlinear Dynamics and Chaos (Addison-Wesley, Massachusetts, 1994)

    Google Scholar 

  57. J. Sun, E.M. Bollt, T. Nishikawa, Master stability functions for coupled nearly identical dynamical systems. Europhys. Lett. 85(6), 60011 (2011)

    Google Scholar 

  58. T.J. Taylor, I.Z. Kiss, Interdependency and hierarchy of exact and approximate epidemic models on networks. J. Math. Biol. 69(1), 183–211 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  59. D. Taylor, F. Klimm, H.A. Harrington, M. Kramár, K. Mischaikow, M.A. Porter, P.J. Mucha, Topological data analysis of contagion maps for examining spreading processes on networks. Nat. Commun. 6, 7723 (2015)

    Article  Google Scholar 

  60. P. Van Mieghem, Graph Spectra for Complex Networks (Cambridge University Press, Cambridge, 2013)

    MATH  Google Scholar 

  61. A. Vespignani, Modelling dynamical processes in complex socio-technical systems. Nat. Phys. 8(1), 32–39 (2012)

    Article  MathSciNet  Google Scholar 

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Porter, M.A., Gleeson, J.P. (2016). General Considerations. In: Dynamical Systems on Networks. Frontiers in Applied Dynamical Systems: Reviews and Tutorials, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-26641-1_4

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