Open AccessCollective Classification in Network Data
Author(s) -
Sen Prithviraj,
Namata Galileo,
Bilgic Mustafa,
Getoor Lise,
Gallagher Brian,
EliassiRad Tina
Publication year - 2008
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v29i3.2157
Subject(s) - computer science , hyperlink , hypertext , friendship , inference , focus (optics) , artificial intelligence , machine learning , world wide web , data science , real world data , social network (sociolinguistics) , web page , social media , psychology , social psychology , physics , optics
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.