Collective Classification in Network Data
Collective Classification in Network Data
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DOI：
10.1201/b17320-16
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发表时间：
2008-09
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影响因子：
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通讯作者：
Prithviraj Sen;Galileo Namata;M. Bilgic;L. Getoor;Brian Gallagher;Tina Eliassi-Rad
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作者：
Prithviraj Sen;Galileo Namata;M. Bilgic;L. Getoor;Brian Gallagher;Tina Eliassi-Rad
许多真实世界的应用程序产生网络数据，例如万维网(通过超链接连接的超文本文档)、社交网络(例如，通过友谊链接连接的人)、通信网络(通过通信链路连接的计算机)和生物网络(例如，蛋白质相互作用网络)。最近机器学习研究的一个焦点是将传统的机器学习分类技术扩展到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情况。我们介绍了四种最广泛使用的用于网络数据分类的推理算法，并在合成数据和真实数据上进行了实证比较。
Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, 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.