Graphical discovery in stochastic actor-oriented models for social network analysis

dc.contributor.advisor Heike Hofmann
dc.contributor.author Tyner, Samantha
dc.contributor.department Statistics
dc.date 2019-01-15T09:20:44.000
dc.date.accessioned 2020-06-30T03:13:21Z
dc.date.available 2020-06-30T03:13:21Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.embargo 2018-11-27
dc.date.issued 2017-01-01
dc.description.abstract <p>This work presented in this thesis combines statistical models for social networks and network visualization in new and exciting ways. In Chapter 1, a thorough review of the literature in the topics of statistical network models and network visualization is presented. In Chapter 2, we focus in on one type of model for dynamic social networks: the stochas- tic actor-oriented models (SAOMs), introduced by Snijders (1996). Unlike other network models, SAOMs are not very well understood, so we use model visualization techniques in- spired by those introduced in Wickham et al (2015) in order to make the models a little less murky. The SAOMs are a prime example of a set of models that can benefit greatly from application of model visualization, and with the help of static and dynamic visualizations, we bring the hidden model fitting processes into the foreground, eventually leading to a better understanding and higher accessibility of stochastic actor-oriented models for social network analysts. In Chapter 3, we further explore the SAOMs using the visual inference methodology of Buja et al. (2009). We construct significance tests of model parameters, goodness-of-fit tests, and power calculations for the objective function parameters in SAOMs using visual inference. In this way, we can explore complex network data more completely than traditional significance and goodness-of-fit methods that rely on one-dimensional de- rived features of networks do. In Chapter 4, we present an R package for drawing networks using the popular grammar of graphics R plotting paradigm, ggplot2 (Wickham 2016). We close with a discussion of the limitations of the work and directions for the future in Chapter 5.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/16751/
dc.identifier.articleid 7758
dc.identifier.contextkey 13578564
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/16751
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/30934
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/16751/Tyner_iastate_0097E_17055.pdf|||Fri Jan 14 21:05:27 UTC 2022
dc.subject.disciplines Statistics and Probability
dc.subject.keywords dynamic networks
dc.subject.keywords network analysis
dc.subject.keywords network visualization
dc.subject.keywords statistical graphics
dc.subject.keywords stochastic actor oriented models
dc.subject.keywords visual inference
dc.title Graphical discovery in stochastic actor-oriented models for social network analysis
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
thesis.degree.discipline Statistics
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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