Lim, Kar WaiBuntine, Wray2025-12-172025-12-171532-4435https://hdl.handle.net/1885/733796052Bibliographic 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.NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. The authors wish to thank CiteSeerX for providing the data.17enPublisher Copyright: © 2014 K.W. Lim & W. Buntine.Author-citation networkBayesian non-parametricTopic model201484984694245