Dynamical Coarse Graining of Large Scale-Free Boolean networks
Abstract
We present a renormalization-grouplike method performed in the state space for detecting the dynamical behaviors of large scale-free Boolean networks, especially for the chaotic regime as well as the edge of chaos. Numerical simulations with different coarse-graining level show that the state space networks of scale-free Boolean networks follow universal power-law distributions of in and out strength, in and out degree, as well as weight. These interesting results indicate scale-free Boolean networks still possess self-organized mechanism near the edge of chaos in the chaotic regime. The number of state nodes as a function of biased parameter for distinct coarse-graining level also demonstrates that the power-law behaviors are not the artifact of coarse-graining procedure. Our work may also shed some light on the investigation of brain dynamics.
Keywords
Cite
@article{arxiv.cond-mat/0603167,
title = {Dynamical Coarse Graining of Large Scale-Free Boolean networks},
author = {Wen-Xu Wang and Gang Yan and Jie Ren and Bing-Hong Wang},
journal= {arXiv preprint arXiv:cond-mat/0603167},
year = {2007}
}
Comments
5 pages, 6 figures