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Exponential random graph models for multilevel networks

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dc.contributor.advisor Polansky, Alan M. en_US
dc.contributor.author Tattersall, Daniel en_US
dc.date.accessioned 2017-08-02T21:16:18Z
dc.date.available 2017-08-02T21:16:18Z
dc.date.issued 2014
dc.identifier.uri http://commons.lib.niu.edu/handle/10843/17760
dc.description Advisors: Alan Polansky. en_US
dc.description Committee members: Sanjib Basu; Nader Ebrahimi. en_US
dc.description.abstract This 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.extent 67 pages en_US
dc.language.iso eng en_US
dc.publisher Northern Illinois University en_US
dc.rights NIU 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.lcsh Social networks--Mathematical models en_US
dc.subject.lcsh Social networks--Statistical methods en_US
dc.subject.lcsh Social networks--Research--Graphic methods en_US
dc.subject.lcsh Statistics en_US
dc.subject.lcsh Mathematics en_US
dc.title Exponential random graph models for multilevel networks en_US
dc.type Text en_US
dc.contributor.department Department of Statistics en_US
dc.description.degree M.S. (Master of Science) en_US


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