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dc.contributor.advisorAnagnostopoulos, Georgios
dc.contributor.authorAravamudan, Akshay
dc.date.accessioned2019-09-13T19:06:54Z
dc.date.available2019-09-13T19:06:54Z
dc.date.created2019-07
dc.date.issued2019-07
dc.date.submittedJuly 2019
dc.identifier.urihttp://hdl.handle.net/11141/2886
dc.descriptionThesis (M.S.) - Florida Institute of Technology, 2019.en_US
dc.description.abstractInformation diffusion is the spread of information within a network. In this thesis, we model information diffusion as a survival process. We have adopted an existing algorithm called NetRate for modelling information diffusion. This model involves finding the distribution of trasmission time between two nodes in the network. We modify NetRate’s concave-down log-likelihood expression by adding partial parentage information and formulate an Expectation-Minimization (EM) algorithm to learn the parameters. We also describe a simulation scheme for NetRate inspired by point process simulation strategies. Using the assumptions of the NetRate model, we derive a a method to model popularity as a function of time. In order to showcase the insights offered by NetRate, we explore a real-world example involving two kinds of software vulnerabilities: Exploited and non-exploited vulnerabilities. Finally, We derive a scheme for transforming infection times so that a goodness of fit test can be performed using Kolmogorov-Smirnov (KS) test statistic.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCopyright held by author.en_US
dc.titleSurvival Theory Modelling for Information Diffusionen_US
dc.typeThesisen_US
dc.date.updated2019-08-22T17:58:03Z
thesis.degree.nameMasters of Science in Computer Engineeringen_US
thesis.degree.levelMastersen_US
thesis.degree.disciplineComputer Engineeringen_US
thesis.degree.departmentComputer Engineering and Sciencesen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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