Connections between the lines: Augmenting social networks with text

Network data is ubiquitous, encoding collections of relation-ships between entities such as people, places, genes, or cor-porations. While many resources for networks of interest-ing entities are emerging, most of these can only annotate connections in a limited fashion. Although relationships be-tween entities are rich, it is impractical to manually devise complete characterizations of these relationships for every pair of entities on large, real-world corpora. In this paper we present a novel probabilistic topic model to analyze text corpora and infer descriptions of its enti-ties and of relationships between those entities. We develop variational methods for performing approximate inference on our model and demonstrate that our model can be prac-tically deployed on large corpora such as Wikipedia. We show qualitatively and quantitatively that our model can construct and annotate graphs of relationships and make useful predictions