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dc.contributor.advisorPolansky, Alan M.en_US
dc.contributor.authorTattersall, Danielen_US
dc.date.accessioned2017-08-02T21:16:18Z
dc.date.available2017-08-02T21:16:18Z
dc.date.issued2014
dc.identifier.urihttp://commons.lib.niu.edu/handle/10843/17760
dc.descriptionAdvisors: Alan Polansky.en_US
dc.descriptionCommittee members: Sanjib Basu; Nader Ebrahimi.en_US
dc.description.abstractThis master's thesis investigates the use of exponential random graph models for multilevel networks. It begins by describing some basic ideas in network analysis and then moves into the use of models to describe observed networks. After establishing modeling concepts for single-level networks, the discussion expands to modeling multilevel networks, which is a less common practice, and provides a brief multilevel modeling application. Focus is given to ERGM theory basics and highlights potential problems that researchers may encounter when employing these methods. Ultimately, the reader leaves with a sense of how and why network complexity can be modeled and some of the challenges that face network research.en_US
dc.format.extent67 pagesen_US
dc.language.isoengen_US
dc.publisherNorthern Illinois Universityen_US
dc.rightsNIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.en_US
dc.subject.lcshSocial networks--Mathematical modelsen_US
dc.subject.lcshSocial networks--Statistical methodsen_US
dc.subject.lcshSocial networks--Research--Graphic methodsen_US
dc.subject.lcshStatisticsen_US
dc.subject.lcshMathematicsen_US
dc.titleExponential random graph models for multilevel networksen_US
dc.typeTexten_US
dc.contributor.departmentDepartment of Statisticsen_US
dc.description.degreeM.S. (Master of Science)en_US


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