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    Instance, Evolution, and Predictive Modeling of Social Networks

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    Author
    Kaza, Siddharth
    Issue Date
    2008
    Keywords
    Management Information Systems
    Advisor
    Chen, Hsinchun
    Committee Chair
    Chen, Hsinchun
    
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    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
    Degree Name
    PhD
    Degree Level
    doctoral
    Degree Program
    Management Information Systems
    Graduate College
    Degree Grantor
    University of Arizona
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