Damage Spreading and Criticality in Finite Random Dynamical Networks

TL;DR

This paper investigates damage spreading in finite random dynamical networks at the sparse percolation limit, identifying a new characteristic connectivity and analyzing how critical points deviate from classical predictions, with implications for biological and technological networks.

Contribution

It introduces a finite size scaling approach to identify a new characteristic connectivity and analyzes the deviation of finite-size critical points from classical theory in damage spreading.

Findings

Discovered a new characteristic connectivity $K_s$ where damage is size-independent.

Finite size critical connectivity $K_c^{sparse}(N)$ deviates from the annealed approximation.

Results have implications for gene regulatory networks and network evolution.

Abstract

We systematically study and compare damage spreading at the sparse percolation (SP) limit for random boolean and threshold networks with perturbations that are independent of the network size $N$. This limit is relevant to information and damage propagation in many technological and natural networks. Using finite size scaling, we identify a new characteristic connectivity $K_s$, at which the average number of damaged nodes $\bar d$, after a large number of dynamical updates, is independent of $N$. Based on marginal damage spreading, we determine the critical connectivity $K_c^{sparse}(N)$ for finite $N$ at the SP limit and show that it systematically deviates from $K_c$, established by the annealed approximation, even for large system sizes. Our findings can potentially explain the results recently obtained for gene regulatory networks and have important implications for the evolution of dynamical networks that solve specific computational or functional tasks.