We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed vocabulary. 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 estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news.
Chang REF develops the relational topic model (RTM), a hierarchical model of both network structure and node attributes, which can be used to summarize a network of documents, predict links between them, and predict words within them.
Hierarchical relational models for document networks
Annals of Applied Statistics 2010, Vol. 4, No. 1, 124-150
Computer Science Mathematics