Tracking infection diffusion in social networks
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Pedersen, Tavis Joseph
issued: 2017-08-10T21:25:14Z
issued: 2017
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: This thesis explores the problem of tracking the diffusion of contagion processes on social networks. Infection (or Information) diffusion is modeled using the Susceptible-Infected-Susceptible (SIS) model. Mean field approximation is employed to approximate the discrete valued infection dynamics by a deterministic ordinary differential equation, thereby yielding a generative model for the infection diffusion. The infection is shown to follow polynomial dynamics and is estimated using an exact non-linear Bayesian filter. We compute posterior Cramer-Rao bounds to obtain the fundamental limits of the filter which depend on the structure of the network. The SIS model is extended to include homophily, and filtering on these networks using the exact non-linear Bayesian filter is illustrated. With consideration for the collaborative or antagonistic nature of some social processes on networks, we present an alternative, game theoretic, model for the spread of information based on the evolutionary Moran process. The diffusion of collaboration following this Moran model is estimated using a particle filter. Additionally, we validate the efficacy of our method with diffusion data from a real-world online social media platform, Twitter. We find that SIS model is a good fit for the information diffusion and the non-linear filter effectively tracks the information diffusion.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/62557
note: Tracking Infection Diffusion inSocial NetworksbyTavis Joseph PedersenBASc. Engineering Physics, University of British Columbia, Canada, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Electrical and Computer Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)August 2017c© Tavis Joseph Pedersen 2017AbstractThis thesis explores the problem of tracking the diffusion of contagion processes on socialnetworks. Infection (or Information) diffusion is modeled using the Susceptible-Infected-Susceptible (SIS) model. Mean field approximation is employed to approximate the discretevalued infection dynamics by a deterministic ordinary differential equation, thereby yieldinga generative model for the infection diffusion. The infection is shown to follow polynomialdynamics and is estimated using an exact non-linear Bayesian filter. We compute posteriorCrame´r-Rao bounds to obtain the fundamental limits of the filter which depend on thestructure of the network. The SIS model is extended to include homophily, and filtering onthese networks using the exact non-linear Bayesian filter is illustrated. With considerationfor the collaborative or antagonistic nature of some social processes on networks, we presentan alternative, game theoretic, model for the spread of information based on the evolutionaryMoran process. The diffusion of collaboration following this Moran model is estimated usinga particle filter. Additionally, we validate the efficacy of our method with diffusion datafrom a real-world online social media platform, Twitter. We find that SIS model is a goodfit for the information diffusion and the non-linear filter effectively tracks the informationdiffusion.iiLay SummaryIn this thesis we use expert knowledge in the form of models for infectious disease in com-bination with statistical algorithms to better understand how an infection is spreading andwho the affl…
has type: Thesis/Dissertation
date issued: 2017-09
is shown at: 10.14288/1.0353169
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/62557