Diffusion Processes on Small-World Networks with Distance-Dependent Random-Links

Details Diffusion Processes on Small-World Networks with Distance-Dependent Random-Links Diffusion Processes on Small-World Networks with Distance-Dependent Random-Links

2007-04-19

arxiv.org/abs/0704.2564v2

J. Stat. Mech. (2007) P08014

Physics

Condensed Matter

Statistical Mechanics

36 pages, 27 figures. Minor revisions in response to the referee's comments. Furthermore, some typos were fixed and new refere

Scientific paper

10.1088/1742-5468/2007/08/P08014

We considered diffusion-driven processes on small-world networks with distance-dependent random links. The study of diffusion on such networks is motivated by transport on randomly folded polymer chains, synchronization problems in task-completion networks, and gradient driven transport on networks. Changing the parameters of the distance-dependence, we found a rich phase diagram, with different transient and recurrent phases in the context of random walks on networks. We performed the calculations in two limiting cases: in the annealed case, where the rearrangement of the random links is fast, and in the quenched case, where the link rearrangement is slow compared to the motion of the random walker or the surface. It has been well-established that in a large class of interacting systems, adding an arbitrarily small density of, possibly long-range, quenched random links to a regular lattice interaction topology, will give rise to mean-field (or annealed) like behavior. In some cases, however, mean-field scaling breaks down, such as in diffusion or in the Edwards-Wilkinson process in "low-dimensional" small-world networks. This break-down can be understood by treating the random links perturbatively, where the mean-field (or annealed) prediction appears as the lowest-order term of a naive perturbation expansion. The asymptotic analytic results are also confirmed numerically by employing exact numerical diagonalization of the network Laplacian. Further, we construct a finite-size scaling framework for the relevant observables, capturing the cross-over behaviors in finite networks. This work provides a detailed account of the self-consistent-perturbative and renormalization approaches briefly introduced in two earlier short reports.

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