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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 paper, the authors attempt to provide a brief introduction to this area of research and how it has progressed during the past decade. They 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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