Persistent Link:
http://hdl.handle.net/10150/193625
Title:
Instance, Evolution, and Predictive Modeling of Social Networks
Author:
Kaza, Siddharth
Issue Date:
2008
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Various phenomena within the information systems discipline can be studied using the social network paradigm that views social entities as nodes with links between them. The social network analysis (SNA) theory has applications in knowledge management, computer mediated communications, security informatics, and other domains. Challenges in SNA can be classified into three broad areas: instance modeling, evolution modeling, and predictive modeling. Instance modeling focuses on the study of static network properties, evolution modeling examines factors behind network growth, and predictive modeling is concerned with identification of hidden and future network links. This dissertation presents four essays that address these challenges with empirical studies in knowledge management and security informatics.The first essay on instance and evolution modeling contributes to SNA theory by examining a real-world network that contains interactions between thirty thousand individuals. The study is among the few that empirically examine large human-only networks and verify the presence of small-world properties and scale-free distributions. In addition, it proposes a novel application of a network evolution model to examine the growth of networks across geographical boundaries.The second essay on evolution modeling proposes a methodology to identify significant link-formation facilitators. The study found that homophily in age, gender, and race were not significant factors in predicting future links between individuals in dark networks. These results contradicted some previous studies in the same domain that used smaller datasets to study the phenomena.The third essay focuses on evolution and predictive modeling and examines the role of inventor status on the selection of knowledge recombined to produce innovation. A new network measure based on random walks and team identification (RWT) is proposed to model knowledge flow. It is found that inventor status as measured by RWT has a positive relationship with the likelihood of a future citation link to the inventor.The fourth essay focuses on predictive modeling. A modified mutual information formulation is proposed to identify hidden links between nodes based on heuristics of time and location of previous co-occurrences. An evaluation of the proposed technique showed that it performed better in predicting hidden links than other co-occurrence based methods.
Type:
text; Electronic Dissertation
Keywords:
Management Information Systems
Degree Name:
PhD
Degree Level:
doctoral
Degree Program:
Management Information Systems; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Chen, Hsinchun
Committee Chair:
Chen, Hsinchun

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleInstance, Evolution, and Predictive Modeling of Social Networksen_US
dc.creatorKaza, Siddharthen_US
dc.contributor.authorKaza, Siddharthen_US
dc.date.issued2008en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractVarious phenomena within the information systems discipline can be studied using the social network paradigm that views social entities as nodes with links between them. The social network analysis (SNA) theory has applications in knowledge management, computer mediated communications, security informatics, and other domains. Challenges in SNA can be classified into three broad areas: instance modeling, evolution modeling, and predictive modeling. Instance modeling focuses on the study of static network properties, evolution modeling examines factors behind network growth, and predictive modeling is concerned with identification of hidden and future network links. This dissertation presents four essays that address these challenges with empirical studies in knowledge management and security informatics.The first essay on instance and evolution modeling contributes to SNA theory by examining a real-world network that contains interactions between thirty thousand individuals. The study is among the few that empirically examine large human-only networks and verify the presence of small-world properties and scale-free distributions. In addition, it proposes a novel application of a network evolution model to examine the growth of networks across geographical boundaries.The second essay on evolution modeling proposes a methodology to identify significant link-formation facilitators. The study found that homophily in age, gender, and race were not significant factors in predicting future links between individuals in dark networks. These results contradicted some previous studies in the same domain that used smaller datasets to study the phenomena.The third essay focuses on evolution and predictive modeling and examines the role of inventor status on the selection of knowledge recombined to produce innovation. A new network measure based on random walks and team identification (RWT) is proposed to model knowledge flow. It is found that inventor status as measured by RWT has a positive relationship with the likelihood of a future citation link to the inventor.The fourth essay focuses on predictive modeling. A modified mutual information formulation is proposed to identify hidden links between nodes based on heuristics of time and location of previous co-occurrences. An evaluation of the proposed technique showed that it performed better in predicting hidden links than other co-occurrence based methods.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectManagement Information Systemsen_US
thesis.degree.namePhDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineManagement Information Systemsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorChen, Hsinchunen_US
dc.contributor.chairChen, Hsinchunen_US
dc.contributor.committeememberNunamaker, Jr., Jay F.en_US
dc.contributor.committeememberZhang, Zhuen_US
dc.identifier.proquest2607en_US
dc.identifier.oclc659749599en_US
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