Asymptotically inspired moment-closure approximation for adaptive networks

TL;DR

This paper introduces an asymptotic-based moment-closure method for adaptive networks, improving mean-field predictions by analytically approximating higher-order structures, demonstrated on recruitment and epidemic models.

Contribution

It presents a novel asymptotic approach to moment closure that enhances mean-field models of adaptive networks, addressing the open system problem.

Findings

Good agreement with network simulations

Improved accuracy over traditional mean-field models

Applicable to diverse adaptive network scenarios

Abstract

Adaptive social networks, in which nodes and network structure co-evolve, are often described using a mean-field system of equations for the density of node and link types. These equations constitute an open system due to dependence on higher order topological structures. We propose a new approach to moment closure based on the analytical description of the system in an asymptotic regime. We apply the proposed approach to two examples of adaptive networks: recruitment to a cause model and adaptive epidemic model. We show a good agreement between the improved mean-field prediction and simulations of the full network system.