计算机科学
超链接
超文本
推论
光学(聚焦)
真实世界数据
友谊
人工智能
机器学习
数据科学
万维网
网页
心理学
社会心理学
光学
物理
作者
Prithviraj Sen,Galileo Namata,Mustafa Bilgic,Lise Getoor,Brian Galligher,Tina Eliassi-Rad
出处
期刊:Ai Magazine
[Association for the Advancement of Artificial Intelligence (AAAI)]
日期:2008-09-01
卷期号:29 (3): 93-106
被引量:1854
标识
DOI:10.1609/aimag.v29i3.2157
摘要
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‐learning 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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