Abstract

Attribute information from social network users can be used as a basis for grouping users, sharing content, and recommending friends. However, in practice, not all users provide their attributes. In this paper, we try to use information from both the graph structure of the social network and the known attributes of users to predict the unknown attributes of users. Considering the topological structure of a social network and the characteristics of users’ data, we select a graph-based semi-supervised learning algorithm to predict users’ attributes. We design different strategies for computing the relational weights between users. The experimental results on real-world data from Renren demonstrate that the semi-supervised learning method is more suitable for predicting users’ attributes compared with the supervised learning models, and our strategies for computing the relational weights between users are effective. We also analyze the effect of different social relations on predicting users’ attributes.