Spreading Processes over Adaptive Networks

Master thesis (2021)

Authors

T. Gan Electrical Engineering, Mathematics and Computer Science

Contributors

P.F.A. Van Mieghem Network Architectures and Services - EEMCS (supervisor 1)
M.A. Achterberg Network Architectures and Services - EEMCS (supervisor 1)
J.L.A. Dubbeldam Mathematical Physics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Tian Gan
More Info
expand_more

Published Date

19-11-2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The spreading process of diseases has been an important research topic for many years. It has profound effects on the development of human social behaviors. The underlying social network structure may change when individuals change their connection with other individuals in response to the epidemic. The classic susceptible-infected-susceptible (SIS) model is used to model the spread of an epidemic on a network, where all individuals are defined as nodes and the connections between the individuals are regarded as links. Besides the typical static network, the structure of the network can be related to the state of nodes (infected or susceptible) by link breaking and link creation processes. So the extended network, adaptive susceptible-infected-susceptible model (ASIS model) can be derived.

To study the spreading process on static networks and adaptive networks, we use stochas- tic simulations and mean field approximations. We assume that the spreading process over the network is a continuous-time discrete-state Markov process. But most recent works use the discrete-time simulator, which is actually an approximation of the process. In this report, we extend a existing continuous-time simulator towards adaptive networks. This existing simulator is based on the Gillespie algorithm. We perform the simulations using both discrete-time Markov chain and continuous-time Markov process. And based on the simulation results, we demonstrate that the continuous-time simulator has a better performance than the discrete-time simulator on modeling both static SIS network and ASIS network with high accuracy.

The second part of this work aims to study the characteristics of the ASIS network in the metastable state. We observe three possible states of the ASIS network: the endemic state, disease-free state and bistable state. The degree distribution of the graph follows a binomial distribution in some cases. By plenty of simulations with different parameters, we illustrate under what circumstances the degree distribution follows the binomial distribution.