Learning a contingently acyclic, probabilistic relational model of a social network

We demonstrate through experimental comparisons that modeling relations in a social network with a directed probabilistic model provides a viable alternative to the standard undirected graphical model approach. Our model incorporates special latent variables to guarantee acyclicity. We investigate the inference and learning challenges entailed by our approach.