Steady-State Dynamics of the Forest Fire Model on Complex Networks

TL;DR

This paper investigates the steady-state behavior of the forest fire model on complex networks, developing a heterogeneous mean-field theory and validating it through simulations to understand its accuracy and limitations.

Contribution

It introduces a heterogeneous mean-field approach to analyze the forest fire model on complex networks, providing exact and approximate solutions for various network types.

Findings

Mean-field theory accurately predicts dynamics in certain parameter regions.

Identifies limits of mean-field approximation when correlations are strong.

Provides analytical tools for epidemic-like processes on complex networks.

Abstract

Many sociological networks, as well as biological and technological ones, can be represented in terms of complex networks with a heterogeneous connectivity pattern. Dynamical processes taking place on top of them can be very much influenced by this topological fact. In this paper we consider a paradigmatic model of non-equilibrium dynamics, namely the forest fire model, whose relevance lies in its capacity to represent several epidemic processes in a general parametrization. We study the behavior of this model in complex networks by developing the corresponding heterogeneous mean-field theory and solving it in its steady state. We provide exact and approximate expressions for homogeneous networks and several instances of heterogeneous networks. A comparison of our analytical results with extensive numerical simulations allows to draw the region of the parameter space in which heterogeneous mean-field theory provides an accurate description of the dynamics, and enlights the limits of validity of the mean-field theory in situations where dynamical correlations become important.