  Bibliographic analysis considers the author's research areas, the citation
network and the 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 nonparametric extension of a combination of the
Poisson mixed-topic link model and the author-topic model. This gives rise to
the Citation Network Topic Model (CNTM). We propose a novel and efficient
inference algorithm for the CNTM to explore subsets of research publications
from CiteSeerX. The publication datasets are organised into three corpora,
totalling to about 168k publications with about 62k authors. The queried
datasets are made available online. In three publicly available corpora in
addition to the queried datasets, our proposed model demonstrates an improved
performance in both model fitting and document clustering, compared to several
baselines. Moreover, our model allows extraction of additional useful knowledge
from the corpora, such as the visualisation of the author-topics network.
Additionally, we propose a simple method to incorporate supervision into topic
modelling to achieve further improvement on the clustering task.
