  Bibliographic analysis considers author's research areas, the citation
network and paper content among other things. In this paper, we combine these
three in a topic model that produces a bibliographic model of authors, topics
and documents using a non-parametric extension of a combination of the Poisson
mixed-topic link model and the author-topic model. We propose a novel and
efficient inference algorithm for the model to explore subsets of research
publications from CiteSeerX. Our model demonstrates improved performance in
both model fitting and a clustering task compared to several baselines.
