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    De-anonymizing Social Network Neighborhoods Using Auxiliary and Semantic Information

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    Morgan-Final.pdf
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    Description:
    Steven Morgan Thesis
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    Author
    Morgan, Steven Michael
    Novillo, Jorge; Adviser
    Andriamanalimanana, Bruno; Reviewer
    Reale, Michael; Reviewer
    Keyword
    social networks
    semantic information
    data mining
    auxiliary information
    textual analysis
    social network data
    Date Published
    2015-12-11
    
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    URI
    http://hdl.handle.net/20.500.12648/1091
    Abstract
    The increasing popularity of social networks and their progressively more robust uses provides an interesting intersection of data. Social graphs have been rigorously studied for de-anonymization. Users of social networks will provide feedback to pages of interest and will create a vibrant profile. In addition to user interests, textual analysis provides another feature set for users. The user profile can be viewed as a classical relational dataset in conjunction with graph data. This paper uses semantic information to improve the accuracy of de-anonymizing social network data.
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    College of Engineering, SUNY Polytechnic Institute

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