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2010 IEEE International Conference on Data Mining (2010)
Sydney, Australia
Dec. 13, 2010 to Dec. 17, 2010
ISSN: 1550-4786
ISBN: 978-0-7695-4256-0
pp: 923-928
Link prediction is a fundamental problem in social network analysis and modern-day commercial applications such as Face book and My space. 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 single-source supervised models.
social network, link prediction, multiple sources, supervised learning

W. Tang, Z. Lu, B. Savas and I. S. Dhillon, "Supervised Link Prediction Using Multiple Sources," 2010 IEEE International Conference on Data Mining(ICDM), Sydney, Australia, 2010, pp. 923-928.
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