2017
DOI: 10.1007/s10955-017-1785-z
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Information Dimension of Stochastic Processes on Networks: Relating Entropy Production to Spectral Properties

Abstract: We consider discrete stochastic processes, modeled by classical master equations, on networks. The temporal growth of the lack of information about the system is captured by its non-equilibrium entropy, defined via the transition probabilities between different nodes of the network. We derive a relation between the entropy and the spectrum of the master equation's transfer matrix. Our findings indicate that the temporal growth of the entropy is proportional to the logarithm of time if the spectral density show… Show more

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Cited by 6 publications
(7 citation statements)
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References 26 publications
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“…This is readily understood since, as said above, the number of excited modes grows as a √ t. This fits with the general fact, that there exist a close relation between the growth of S and the spectrum of the master equation on networks (see e.g. [72] and the references therein) . If the spectral density of the operator shows scaling the entropy grows as ds 2 log t with time before reaching the equipartition value.…”
Section: Spectral Entropysupporting
confidence: 77%
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“…This is readily understood since, as said above, the number of excited modes grows as a √ t. This fits with the general fact, that there exist a close relation between the growth of S and the spectrum of the master equation on networks (see e.g. [72] and the references therein) . If the spectral density of the operator shows scaling the entropy grows as ds 2 log t with time before reaching the equipartition value.…”
Section: Spectral Entropysupporting
confidence: 77%
“…As we have shown above, the energy transfer processes in the translation-invariant models occurs through a gradual, global, redistribution of the initial energy towards all the other modes. In the intermediate times, and assuming all the energy initially in one single mode E ν 0 (0) = δ ν,ν 0 for simplicity, one could thus perform a kind of "meanfield" approximation where [72]…”
Section: Spectral Entropymentioning
confidence: 99%
“…The eigenvalue spectrum of the NT D trees is nonhomogenous. 30,32 It is dominated by the behavior of the linear chains characterized by the spectral dimension d s = 1. However, the smallest eigenvalues that describe large relaxation times (which correspond to the relaxation of the branches and subbranches as a whole [51][52][53] and related to large-scale characteristics such as gyration radius at the thermal equilibrium 45 ) march to a different tune.…”
Section: Nt D Treesmentioning
confidence: 99%
“…Under this approximation the integral can be readily computed, yielding a time dependence proportional to t α . This time regime holds for t τ 0 /λ we divide the integral of eqn (32) into three parts and use the small and long time behaviors of the Mittag-Leffler function discussed above,…”
Section: Analysis Of σ 2 (T)mentioning
confidence: 99%
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