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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorGao, Jianxi
dc.contributorAdali, Sibel
dc.contributorHolzbauer, Buster
dc.contributor.authorTrujillo, Milo Zappa
dc.date.accessioned2021-11-03T09:17:04Z
dc.date.available2021-11-03T09:17:04Z
dc.date.created2020-08-13T11:46:15Z
dc.date.issued2020-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2519
dc.descriptionMay 2020
dc.descriptionSchool of Science
dc.description.abstractInformation Cascades are propagation patterns in human networks where information such as political propaganda, rumors, or fashion trends, emanate from one or more starting points and are either accepted or rejected by each connected member of a community. The decisions made by individuals either govern a large ripple through the community, or prevent significant changes from occurring, making Information Cascades of avid interest within economics and network science. Since Information Cascades have similar behavior to the well studied field of epidemiology, many Cascades are modeled with disease propagation models like the Susceptibility, Infection, and Recovery (SIR) model. In this thesis we argue that such generic models are insufficient for predicting information spread in human behavioral networks, because they do not represent temporal availability of communication and individual variations in susceptibility to peer-pressure. We propose a new model wherein agents have heterogeneous activity periods and activation thresholds, representing individual susceptibility to peer-pressure. By applying this model to networks with a range of topologies, as well as real-world networks, we are able to more accurately represent human behavior in a variety of communities.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectComputer science
dc.titleInformation contagion in temporal human networks with heterogeneous susceptibility
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid180032
dc.digitool.pid180033
dc.digitool.pid180034
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Computer Science


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