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dc.contributor.authorZhao, Meng Johnen_US
dc.date.accessioned2017-10-28T08:00:19Z
dc.date.available2017-10-28T08:00:19Z
dc.date.issued2017-10-27en_US
dc.identifier.othervt_gsexam:12688en_US
dc.identifier.urihttp://hdl.handle.net/10919/79849
dc.description.abstractAs social networks become more prevalent, there is significant interest in studying these network data, the focus often being on detecting anomalous events. This area of research is referred to as social network surveillance or social network change detection. While there are a variety of proposed methods suitable for different monitoring situations, two important issues have yet to be completely addressed in network surveillance literature. First, performance assessments using simulated data to evaluate the statistical performance of a particular method. Second, the study of aggregated data in social network surveillance. The research presented tackle these issues in two parts, evaluation of a popular anomaly detection method and investigation of the effects of different aggregation levels on network anomaly detection.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectchange detectionen_US
dc.subjectErdos-Renyi modelen_US
dc.subjectmoving windowen_US
dc.subjectsocial networken_US
dc.subjectstandardizationen_US
dc.subjectstatistical process monitoringen_US
dc.subjectaggregationen_US
dc.subjectDCSBMen_US
dc.subjectscan methoden_US
dc.titleAnalysis and Evaluation of Social Network Anomaly Detectionen_US
dc.typeDissertationen_US
dc.contributor.departmentStatisticsen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineStatisticsen_US
dc.contributor.committeechairWoodall, William H.en_US
dc.contributor.committeechairDriscoll, Anne Ryanen_US
dc.contributor.committeememberStevens, Nathaniel Tyleren_US
dc.contributor.committeememberFricker, Ronald D.en_US
dc.contributor.committeememberSengupta, Srijanen_US


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