Collective classification in network data.: An article from: AI Magazine
Book Details
Author(s)Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, Tina Eliassi-Rad
ISBN / ASINB001O1F9DW
ISBN-13978B001O1F9D1
MarketplaceFrance 🇫🇷
Description
This digital document is an article from AI Magazine, published by American Association for Artificial Intelligence on September 22, 2008. The length of the article is 9585 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser.
From the author: 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.
Citation Details
Title: Collective classification in network data.
Author: Prithviraj Sen
Publication:AI Magazine (Magazine/Journal)
Date: September 22, 2008
Publisher: American Association for Artificial Intelligence
Volume: 29 Issue: 3 Page: 93(14)
Distributed by Gale, a part of Cengage Learning
From the author: 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.
Citation Details
Title: Collective classification in network data.
Author: Prithviraj Sen
Publication:AI Magazine (Magazine/Journal)
Date: September 22, 2008
Publisher: American Association for Artificial Intelligence
Volume: 29 Issue: 3 Page: 93(14)
Distributed by Gale, a part of Cengage Learning
