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    Collective Classification in Network Data

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    No. of downloads: 7098

    日付
    2008-02-13
    著者
    Sen, Prithviraj
    Namata, Galileo
    Bilgic, Mustafa
    Getoor, Lise
    Gallagher, Brian
    Eliassi-Rad, Tina
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    Numerous real-world applications produce networked data such as web data (hypertext documents connected via hyperlinks) and communication networks (people connected via communication links). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such data. In this report, we attempt to provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
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    http://hdl.handle.net/1903/7546
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    • Technical Reports from UMIACS
    • Technical Reports of the Computer Science Department

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