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Supervised Link Prediction Using Multiple Sources
Microsoft Research Asia.
The University of Texas at Austin.ORCID iD: 0000-0002-1542-2690
The University of Texas at Austin.
The University of Texas at Austin.
2010 (English)In: Proceedings of the IEEE International Conference on Data Mining (ICDM), 2010, 923-928 p.Conference paper (Refereed)
Abstract [en]

Link prediction is a fundamental problem in social network analysis and modern-day commercial applications such as Facebook and Myspace. Most existing research approaches this problem by exploring the topological structure of a social network using only one source of information. However, in many application domains, in addition to the social network of interest, there are a number of auxiliary social networks and/or derived proximity networks available. The contribution of the paper is twofold: (1) a supervised learning framework that can effectively and efficiently learn the dynamics of social networks in the presence of auxiliary networks; (2) a feature design scheme for constructing a rich variety of path-based features using multiple sources, and an effective feature selection strategy based on structured sparsity. Extensive experiments on three real-world collaboration networks show that our model can effectively learn to predict new links using multiple sources, yielding higher prediction accuracy than unsupervised and singlesource supervised models.

Place, publisher, year, edition, pages
2010. 923-928 p.
National Category
Computational Mathematics Probability Theory and Statistics Other Computer and Information Science
URN: urn:nbn:se:liu:diva-73725DOI: 10.1109/ICDM.2010.112OAI: diva2:476340
10th International Conference on Data Mining (ICDM), 13-17 Dec. 2010, Sydney, NSW
Available from: 2012-01-12 Created: 2012-01-12 Last updated: 2013-10-11

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Savas, Berkant
Computational MathematicsProbability Theory and StatisticsOther Computer and Information Science

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