Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks
Physics and Society
2016-03-23 v1 Social and Information Networks
Adaptation and Self-Organizing Systems
Molecular Networks
Abstract
Random Boolean networks (RBNs) are frequently employed for modelling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system consisting of distinct adaptive RBNs - subnetworks - connected by a set of permanent interlinks. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. We investigate mean node information, mean edge information as well as a mean node degree as functions of model parameters and demonstrate HARBN's ability to describe complex hierarchical systems.
Cite
@article{arxiv.1502.03338,
title = {Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks},
author = {Piotr J. Gorski and Agnieszka Czaplicka and Janusz A. Holyst},
journal= {arXiv preprint arXiv:1502.03338},
year = {2016}
}
Comments
9 pages, 6 figures