Show simple item record

dc.contributor.advisorObradovic, Zoran
dc.creatorOuzienko, Vladimir
dc.date.accessioned2020-11-02T14:46:23Z
dc.date.available2020-11-02T14:46:23Z
dc.date.issued2012
dc.identifier.other864885679
dc.identifier.urihttp://hdl.handle.net/20.500.12613/2056
dc.description.abstractThe heightened research activity in the interdisciplinary field of network science can be attributed to the emergence of the social network computer applications. Researchers understood early on that data describing how entities interconnect is highly valuable and that it offers a deeper understanding about the entities themselves. This is why there were so many studies done about various kinds of networks in the last 10-15 years. The study of the networks from the perspective of computer science usually has two objectives. The first objective is to develop statistical mechanisms capable of accurately describing and modeling observed real-world networks. A good fit of such mechanism suggests the correctness of the model's assumptions and leads to better understanding of the network. A second goal is more practical, a well performing model can be used to predict what will happen to the network in the future. Also, such model can be leveraged to use the information gleaned from network to predict what will happen to the networks entities. One important leitmotif of network research and analysis is wide adaptation of log linear models. In this work we apply this philosophy for study and evaluation of log-linear statistical models in various types of networks. We begin with proposal of the new Temporal Exponential Random Graph Model (tERGM) for the analysis and predictions in the binary temporal social networks. We then extended the model for applications in partially observed networks that change over time. Lastly, we generalize the tERGM model to predict the real-valued weighted links in the temporal non-social networks. The log-linear models are not limited to networks that change over time but can also be applied to networks that are static. One such static network is a social network composed of patients undergoing hemodialysis. Hemodialysis is prescribed to people suffering from the end stage renal disease; the treatment necessitates the attendance, on non-changing schedule, of the hemodialysis clinic for a prolonged time period and this is how the social ties are formed. The new log-linear Social Latent Vectors (SLV) model was applied to study such static social networks. The results obtained from SLV experiments suggest that social relationships formed by patients bear influence on individual patients clinical outcome. The study demonstrates how social network analysis can be applied to better understand the network constituents.
dc.format.extent122 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectComputer Science
dc.subjectErgm
dc.subjectImputation
dc.subjectTemoral Networks
dc.subjectWeighted Networks
dc.titleLog Linear Models for Prediction and Analysis of Networks
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberYates, Alexander P.
dc.contributor.committeememberGillespie, Avrum
dc.contributor.committeememberMegalooikonomou, Vasileios
dc.description.departmentComputer and Information Science
dc.relation.doihttp://dx.doi.org/10.34944/dspace/2038
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
refterms.dateFOA2020-11-02T14:46:23Z


Files in this item

Thumbnail
Name:
Ouzienko_temple_0225E_11314.pdf
Size:
790.5Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record