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MAXIMIZING INFLUENCE OF SIMPLE AND COMPLEX CONTAGION ON REAL-WORLD NETWORKS

dc.contributor.advisorSrinivasan, Aravinden_US
dc.contributor.authorMoores, Geoffreyen_US
dc.date.accessioned2020-09-25T05:30:53Z
dc.date.available2020-09-25T05:30:53Z
dc.date.issued2020en_US
dc.identifierhttps://doi.org/10.13016/ons7-uwtz
dc.identifier.urihttp://hdl.handle.net/1903/26410
dc.description.abstractContagion spread over networks is used to model many important real-world processes from a wide variety of domains including epidemiology, marketing, and systems engineering. A large body of research provides strong theoretical guarantees on simple contagion models, but recent research identifies many real-world processes that feature complex contagions whose spread may depend on multiple exposures or other complex criteria. We present a rigorous study of real-world and artificial networks across simple and complex contagion models. We identify domain-dependent features of real-world networks extracted from publicly-available networks as a guide to solving contagion-related decision problems. We then examine the performance of multiple influence-maximization algorithms across a space of networks and contagion models to develop an experimentally justified guide of best practices for related problems. In particular, genetic algorithms are an extremely viable candidate for these problems, especially with complex graphs and processes.en_US
dc.language.isoenen_US
dc.titleMAXIMIZING INFLUENCE OF SIMPLE AND COMPLEX CONTAGION ON REAL-WORLD NETWORKSen_US
dc.typeThesisen_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.pquncontrolledCommunity Detectionen_US
dc.subject.pquncontrolledComplex Contagionen_US
dc.subject.pquncontrolledGenetic Algorithmsen_US
dc.subject.pquncontrolledInfluence Maximizationen_US
dc.subject.pquncontrolledReal-World Networksen_US


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