Scaling in a general class of critical random Boolean networks

TL;DR

This paper analytically derives the scaling behavior of nonfrozen and relevant nodes in critical random Boolean networks, revealing how these quantities grow with network size and characterizing the structure of relevant components.

Contribution

It provides a general analytical framework for understanding the scaling of nonfrozen and relevant nodes in critical Boolean networks with arbitrary input number and Boolean function distribution.

Findings

Number of nonfrozen nodes scales as N^{2/3}

Relevant nodes increase as N^{1/3}

Most relevant components are simple loops

Abstract

We derive analytically the scaling behavior in the thermodynamic limit of the number of nonfrozen and relevant nodes in the most general class of critical Kauffman networks for any number of inputs per node, and for any choice of the probability distribution for the Boolean functions. By defining and analyzing a stochastic process that determines the frozen core we can prove that the mean number of nonfrozen nodes in any critical network with more than one input per node scales with the network size $N$ as $N^{2/3}$, with only $N^{1/3}$ nonfrozen nodes having two nonfrozen inputs and the number of nonfrozen nodes with more than two inputs being finite in the thermodynamic limit. Using these results we can conclude that the mean number of relevant nodes increases for large $N$ as $N^{1/3}$, with only a finite number of relevant nodes having two relevant inputs, and a vanishing fraction of nodes having more than three of them. It follows that all relevant components apart from a finite number are simple loops, and that the mean number and length of attractors increases faster than any power law with network size.