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dc.contributor.advisorSrinivasan, Aravinden_US
dc.contributor.authorDuBois, Thomas M.en_US
dc.date.accessioned2011-07-07T05:50:16Z
dc.date.available2011-07-07T05:50:16Z
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1903/11720
dc.description.abstractProbabilistic connectivity problems arise naturally in many social networks. In particular the spread of an epidemic across a population and social trust inference motivate much of our work. We examine problems where some property, such as an infection or influence, starts from some initially seeded set of nodes and every affected node transmits the property to its neighbors with a probability determined by the connecting edge. Many problems in this area involve connectivity in a random- graph - the probability of a node being affected is the probability that there is a path to it in the random-graph from one of the seed nodes. We may wish to aid, disrupt, or simply monitor this connectivity. In our core applications, public health officials wish to minimize an epidemic's spread over a population, and connectivity in a social network suggests how closely tied its users are. In support of these and other applications, we study several combinatorial optimization problems on random-graphs. We derive algorithms and demonstrate their effectiveness through simulation, mathematical proof, or both.en_US
dc.titleUSING AND MANIPULATING PROBABILISTIC CONNECTIVITY IN SOCIAL NETWORKSen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pquncontrolledepidemiologyen_US
dc.subject.pquncontrolledprobabilityen_US
dc.subject.pquncontrolledsocial networken_US
dc.subject.pquncontrolledtrust inferenceen_US


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