Collective Classification in Network Data


  • Prithviraj Sen University of Maryland
  • Galileo Namata University of Maryland
  • Mustafa Bilgic University of Maryland
  • Lise Getoor University of Maryland
  • Brian Galligher University of Maryland
  • Tina Eliassi-Rad University of Maryland



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.

Author Biography

Lise Getoor, University of Maryland

Department of Computer Science Associate Professor




How to Cite

Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., & Eliassi-Rad, T. (2008). Collective Classification in Network Data. AI Magazine, 29(3), 93.