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Predicting Interests of People on Online Social Networks

We introduce a new data set which contains both a self-declared friendship network and self-chosen attributes from a finite list defined by the social networking site. We propose Gaussian Field Harmonic Functions (GFHF), a state-of-the-art graph transduction algorithm, as a novel way of testing the relevance of the friendship network for predicting individual attributes. We show that the underlying self-declared friendship network allows us to predict some but not all attributes. We use Support Vector Machines (SVM) in conjunction with GFHF to show that other attributes such as age or languages spoken are also important.