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    Log Linear Models for Prediction and Analysis of Networks

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    Ouzienko_temple_0225E_11314.pdf
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    Genre
    Thesis/Dissertation
    Date
    2012
    Author
    Ouzienko, Vladimir
    Advisor
    Obradovic, Zoran
    Committee member
    Yates, Alexander
    Gillespie, Avrum
    Megalooikonomou, Vasilis
    Department
    Computer and Information Science
    Subject
    Computer Science
    Ergm
    Imputation
    Temoral Networks
    Weighted Networks
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/2056
    
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    DOI
    http://dx.doi.org/10.34944/dspace/2038
    Abstract
    The 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.
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