Optimization Problems for Maximizing Influence in Social Networks

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dc.contributor.advisorWu, Weili
dc.creatorGhosh, Smita
dc.date.accessioned2022-10-12T20:26:30Z
dc.date.available2022-10-12T20:26:30Z
dc.date.created2020-05
dc.date.issued2020-04-21
dc.date.submittedMay 2020
dc.date.updated2022-10-12T20:26:31Z
dc.description.abstractSocial Networks have become very popular in the past decade. They started as platforms to stay connected with friends and family living in different parts of the world, but have evolved into so much more, resulting in Social Network Analysis (SNA) becoming a very popular area of research. One popular problem under the umbrella of SNA is Influence Maximization (IM), which aims at selecting k initially influenced nodes (users) in a social network that will maximize the expected number of eventually-influenced nodes (users) in the network. Influence maximization finds its application in many domains, such as viral marketing, content maximization, epidemic control, virus eradication, rumor control and misinformation blocking. In this dissertation, we study various variations of the IM problem such as Composed Influence Maximization, Group Influence Maximization, Profit Maximization in Groups and Rumor Blocking Problem in Social Networks. We formulate objective functions for these problems and as most of them are NP-hard, we focus on finding methods that ensure efficient estimation of these functions. The two main challenges we face are submodularity and scalibility. To design efficient algorithms, we perform simulations with sampling techniques to improve the effectiveness of our solution approach.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10735.1/9489
dc.language.isoen
dc.subjectSocial networks
dc.subjectSocial influence
dc.subjectRumor
dc.subjectCollective behavior
dc.titleOptimization Problems for Maximizing Influence in Social Networks
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentComputer Science
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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