Moment-Closure Approximations for Discrete Adaptive Networks

TL;DR

This paper surveys various moment-closure approximation schemes applied to the adaptive voter model, analyzing their effectiveness and limitations in capturing network dynamics, and introduces a simple, more effective approximation for challenging parameter ranges.

Contribution

It provides a comprehensive review of existing approximation methods, identifies their failure points, and proposes a new simple scheme that performs better in difficult scenarios.

Findings

Simple schemes give qualitative results

Complex schemes fail quantitatively

A new approximation improves accuracy in challenging parameters

Abstract

Moment-closure approximations are an important tool in the analysis of the dynamics on both static and adaptive networks. Here, we provide a broad survey over different approximation schemes by applying each of them to the adaptive voter model. While already the simplest schemes provide reasonable qualitative results, even very complex and sophisticated approximations fail to capture the dynamics quantitatively. We then perform a detailed analysis that identifies the emergence of specific correlations as the reason for the failure of established approaches, before presenting a simple approximation scheme that works best in the parameter range where all other approaches fail. By combining a focused review of published results with new analysis and illustrations, we seek to build up an intuition regarding the situations when existing approaches work, when they fail, and how new approaches can be tailored to specific problems.