Collective classification in network data

Collective classification in network data (2008)

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by Prithviraj Sen , Galileo Namata , Mustafa Bilgic , Lise Getoor , Brian Gallagher , Tina Eliassi-rad

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Datum	Value	Source
TITLE	Collective classification in network data	INFERENCE
AUTHOR NAME	Prithviraj Sen	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Maryland, College Park	SVM HeaderParse 0.2
AUTHOR NAME	Galileo Namata	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Maryland, College Park	SVM HeaderParse 0.2
AUTHOR NAME	Mustafa Bilgic	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Maryland, College Park	SVM HeaderParse 0.2
AUTHOR NAME	Lise Getoor	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Maryland, College Park	SVM HeaderParse 0.2
AUTHOR NAME	Brian Gallagher	SVM HeaderParse 0.2
AUTHOR AFFIL	Lawrence Livermore National Laboratory	SVM HeaderParse 0.2
AUTHOR NAME	Tina Eliassi-rad	SVM HeaderParse 0.2
AUTHOR AFFIL	Lawrence Livermore National Laboratory	SVM HeaderParse 0.2
ABSTRACT	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. 1	SVM HeaderParse 0.2
YEAR	2008	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical Report CS-TR-4905	INFERENCE
CITATIONS	64 found	ParsCit 1.0