Kinetic-growth self-avoiding walks on small-world networks

Details Kinetic-growth self-avoiding walks on small-world networks Kinetic-growth self-avoiding walks on small-world networks

2008-04-14

arxiv.org/abs/0804.2137v1

Eur. Phys. J. B 56, 71 (2007)

Physics

Condensed Matter

Disordered Systems and Neural Networks

10 pages, 7 figures

Scientific paper

10.1140/epjb/e2007-00086-6

Kinetically-grown self-avoiding walks have been studied on Watts-Strogatz small-world networks, rewired from a two-dimensional square lattice. The maximum length L of this kind of walks is limited in regular lattices by an attrition effect, which gives finite values for its mean value < L >. For random networks, this mean attrition length < L > scales as a power of the network size, and diverges in the thermodynamic limit (large system size N). For small-world networks, we find a behavior that interpolates between those corresponding to regular lattices and randon networks, for rewiring probability p ranging from 0 to 1. For p < 1, the mean self-intersection and attrition length of kinetically-grown walks are finite. For p = 1, < L > grows with system size as N^{1/2}, diverging in the thermodynamic limit. In this limit and close to p = 1, the mean attrition length diverges as (1-p)^{-4}. Results of approximate probabilistic calculations agree well with those derived from numerical simulations.

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