A Discriminative Topic Model Using Document Network Structure

Document collections often have links between documents-citations, hyperlinks, or revisions-and which links are added is often based on topical similarity. To model these intuitions, we introduce a new topic model for documents situated within a network structure, integrating latent blocks of documents with a max-margin learning criterion for link prediction using topic- and word-level features. Experiments on a scientific paper dataset and collection of webpages show that, by more robustly exploiting the rich link structure within a document network, our model improves link prediction, topic quality, and block distributions.