Collective Classification in Network Data

2008/09/01 by Prithviraj Sen, Galileo Namata, Mustafa Bilgic +3 · 38 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques

paper · pdf · doi:10.1609/aimag.v29i3.2157

openalex publication_date 2008/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

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.

Citations

• Expectation Propagation for approximate Bayesian inference
• Birds of a Feather: Homophily in Social Networks

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