Population dynamics on random networks: simulations and analytical models

TL;DR

This paper investigates the phase diagrams of population dynamics models on networks, revealing the limitations of standard pair approximation in capturing real population oscillations and their dependence on network degree.

Contribution

It demonstrates the existence and disappearance of oscillatory phases in models on networks and highlights the failure of standard pair approximation to match simulation results.

Findings

Oscillatory phase exists for small network degree k

Simulations show damping or extinction, not sustained oscillations

Standard pair approximation fails to predict observed behaviors

Abstract

We study the phase diagram of the standard pair approximation equations for two different models in population dynamics, the susceptible-infective-recovered-susceptible model of infection spread and a predator-prey interaction model, on a network of homogeneous degree $k$. These models have similar phase diagrams and represent two classes of systems for which noisy oscillations, still largely unexplained, are observed in nature. We show that for a certain range of the parameter $k$ both models exhibit an oscillatory phase in a region of parameter space that corresponds to weak driving. This oscillatory phase, however, disappears when $k$ is large. For $k=3, 4$, we compare the phase diagram of the standard pair approximation equations of both models with the results of simulations on regular random graphs of the same degree. We show that for parameter values in the oscillatory phase, and even for large system sizes, the simulations either die out or exhibit damped oscillations, depending on the initial conditions. We discuss this failure of the standard pair approximation model to capture even the qualitative behavior of the simulations on large regular random graphs and the relevance of the oscillatory phase in the pair approximation diagrams to explain the cycling behavior found in real populations.