Relational Topic Models for Document Networks
Jonathan Chang, David Blei; JMLR W&CP 5:81-88, 2009.
We develop the relational topic model (RTM), a model of documents and the links between them. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efﬁcient inference and learning algorithms based on variational methods and evaluate the predictive performance of the RTM for large networks of scientiﬁc abstracts and web documents.