Social circle discovery in ego-networks by mining the latent structure of user connections and profile attributes

Online Social Networks (OSN) allow their users to organize their friends into groups, also known as social circles. These social circles can be used to better manage who has access to users' posted content and also to control the content posted from other users that they view. Unfortunately, these social circles are generated manually and this can be a laborious process for users with more than a few friends. In this paper, we propose an approach for automatically generating social circles that takes into account both the profile information of the friends to be grouped and the social network connectivity between them, while it allows multiple membership of friends in social circles. The approach is based on an adaptation of the widely used Latent Dirichlet Allocation model and, despite the fact that it does not explicitly model social network connectivity, as other state of the art methods do, it manages to achieve results that are competitive and even better than those obtained from such methods, at a considerably lower computational cost.