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Collective Classification in Network Data

dc.contributor.authorSen, Prithviraj
dc.contributor.authorNamata, Galileo
dc.contributor.authorBilgic, Mustafa
dc.contributor.authorGetoor, Lise
dc.contributor.authorGallagher, Brian
dc.contributor.authorEliassi-Rad, Tina
dc.date.accessioned2008-02-18T17:56:56Z
dc.date.available2008-02-18T17:56:56Z
dc.date.issued2008-02-13
dc.identifier.urihttp://hdl.handle.net/1903/7546
dc.description.abstractNumerous 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.en
dc.format.extent175688 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.relation.ispartofseriesUM Computer Science Departmenten
dc.relation.ispartofseriesCS-TR-4905en
dc.relation.ispartofseriesUMIACSen
dc.relation.ispartofseriesUMIACS-TR-2008-04en
dc.titleCollective Classification in Network Dataen
dc.typeTechnical Reporten


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