Department of Science and Technology
Link prediction and affiliation recommendation are fundamental problems in social network analysis and modern-day commercial applications. 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 networks and/or derived proximity networks available. In this talk we will present a few ideas on how to utilise auxiliary sources of information for the task at hand. In particular we will present: (1) a supervised learning framework that can 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 auxiliary sources, and (3) an effective feature selection strategy based on structured sparsity. Experiments on real-world networks show that our models can effectively learn to predict new links/recommendation using auxiliary sources, yielding higher prediction accuracy than unsupervised and single source supervised models.