  Social graphs, representing online friendships among users, are one of the
fundamental types of data for many applications, such as recommendation,
virality prediction and marketing in social media. However, this data may be
unavailable due to the privacy concerns of users, or kept private by social
network operators, which makes such applications difficult. Inferring user
interests and discovering user connections through their shared multimedia
content has attracted more and more attention in recent years. This paper
proposes a Gaussian relational topic model for connection discovery using user
shared images in social media. The proposed model not only models user
interests as latent variables through their shared images, but also considers
the connections between users as a result of their shared images. It explicitly
relates user shared images to user connections in a hierarchical, systematic
and supervisory way and provides an end-to-end solution for the problem. This
paper also derives efficient variational inference and learning algorithms for
the posterior of the latent variables and model parameters. It is demonstrated
through experiments with over 200k images from Flickr that the proposed method
significantly outperforms the methods in previous works.
