Adaptive networks: coevolution of disease and topology

TL;DR

This paper introduces an improved compartmental formalism for adaptive networks that accurately models the coevolution of disease spread and network topology, revealing complex dynamical behaviors.

Contribution

It presents a novel compartmental approach that successfully captures the simultaneous evolution of disease states and network structure in adaptive networks.

Findings

Accurately models disease and topology coevolution

Reveals complex dynamical features of adaptive networks

Handles different initial degree distributions

Abstract

Adaptive networks have been recently introduced in the context of disease propagation on complex networks. They account for the mutual interaction between the network topology and the states of the nodes. Until now, existing models have been analyzed using low-complexity analytic formalisms, revealing nevertheless some novel dynamical features. However, current methods have failed to reproduce with accuracy the simultaneous time evolution of the disease and the underlying network topology. In the framework of the adaptive SIS model of Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)], we introduce an improved compartmental formalism able to handle this coevolutionary task successfully. With this approach, we analyze the interplay and outcomes of both dynamical elements, process and structure, on adaptive networks featuring different degree distributions at the initial stage.