Journal
AI MAGAZINE
Volume 29, Issue 3, Pages 93-106Publisher
AMER ASSOC ARTIFICIAL INTELL
DOI: 10.1609/aimag.v29i3.2157
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Funding
- National Science Foundation [0308030]
- Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0308030] Funding Source: National Science Foundation
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Many real-world applications produce networked data such as the worldwide web (hypertext documents connected through hyperlinks), social networks (such as people connected by friendship links), communication networks (computers connected through communication links), and biological networks (such as protein interaction networks). A recent focus in machine-learnings research has been to extend traditional machine-learning classification techniques to classify nodes in such networks. In this article, we 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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