Collective Classification in Network Data
Collective Classification in Network Data
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DOI：
10.1609/aimag.v29i3.2157
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发表时间：
2008-09-01
期刊：
影响因子：
0.9
通讯作者：
Eliassi-Rad, Tina
中科院分区：
文献类型：
--
作者：
Sen, Prithviraj;Namata, Galileo;Eliassi-Rad, Tina
许多现实世界的应用程序产生网络数据，如万维网（通过超链接连接的超文本文档），社交网络（如通过友谊链接连接的人），通信网络（通过通信链接连接的计算机）和生物网络（如蛋白质相互作用网络）。最近机器学习研究的一个焦点是扩展传统的机器学习分类技术来对此类网络中的节点进行分类。在这篇文章中，我们简要介绍了这一领域的研究以及它在过去十年中的进展。我们介绍了四个最广泛使用的推理算法分类网络数据和经验比较合成和真实世界的数据。
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.