document: Connections between the lines : augmenting social networks with text Network data is ubiquitous , encoding collections of relationships between entities such as people , places , genes , or corporations . While many resources for networks of interesting entities are emerging , most of these can only annotate connections in a limited fashion . Although relationships between 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 entities and of relationships between those entities . We develop variational methods for performing approximate inference on our model and demonstrate that our model can be practically 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 .
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