Publication Date

2014

Document Type

Dissertation/Thesis

First Advisor

Polansky, Alan M.

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Statistics

LCSH

Social networks--Mathematical models; Social networks--Statistical methods; Social networks--Research--Graphic methods; Statistics; Mathematics

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.

Comments

Advisors: Alan Polansky.||Committee members: Sanjib Basu; Nader Ebrahimi.

Extent

67 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

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.

Media Type

Text

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