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Prediction, evolution and privacy in social and affiliation networks

dc.contributor.advisorGetoor, Liseen_US
dc.contributor.authorZheleva, Elenaen_US
dc.date.accessioned2011-10-08T06:25:14Z
dc.date.available2011-10-08T06:25:14Z
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1903/12042
dc.description.abstractIn the last few years, there has been a growing interest in studying online social and affiliation networks, leading to a new category of inference problems that consider the actor characteristics and their social environments. These problems have a variety of applications, from creating more effective marketing campaigns to designing better personalized services. Predictive statistical models allow learning hidden information automatically in these networks but also bring many privacy concerns. Three of the main challenges that I address in my thesis are understanding 1) how the complex observed and unobserved relationships among actors can help in building better behavior models, and in designing more accurate predictive algorithms, 2) what are the processes that drive the network growth and link formation, and 3) what are the implications of predictive algorithms to the privacy of users who share content online. The majority of previous work in prediction, evolution and privacy in online social networks has concentrated on the single-mode networks which form around user-user links, such as friendship and email communication. However, single-mode networks often co-exist with two-mode affiliation networks in which users are linked to other entities, such as social groups, online content and events. We study the interplay between these two types of networks and show that analyzing these higher-order interactions can reveal dependencies that are difficult to extract from the pair-wise interactions alone. In particular, we present our contributions to the challenging problems of collective classification, link prediction, network evolution, anonymization and preserving privacy in social and affiliation networks. We evaluate our models on real-world data sets from well-known online social networks, such as Flickr, Facebook, Dogster and LiveJournal.en_US
dc.titlePrediction, evolution and privacy in social and affiliation 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.pqcontrolledArtificial intelligenceen_US
dc.subject.pquncontrolledevolutionen_US
dc.subject.pquncontrolledgraphical modelsen_US
dc.subject.pquncontrolledgroupsen_US
dc.subject.pquncontrolledmachine learningen_US
dc.subject.pquncontrolledprivacyen_US
dc.subject.pquncontrolledsocial networksen_US


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