  Information hierarchies are organizational structures that often used to
organize and present large and complex information as well as provide a
mechanism for effective human navigation. Fortunately, many statistical and
computational models exist that automatically generate hierarchies; however,
the existing approaches do not consider linkages in information {\em networks}
that are increasingly common in real-world scenarios. Current approaches also
tend to present topics as an abstract probably distribution over words, etc
rather than as tangible nodes from the original network. Furthermore, the
statistical techniques present in many previous works are not yet capable of
processing data at Web-scale. In this paper we present the Hierarchical
Document Topic Model (HDTM), which uses a distributed vertex-programming
process to calculate a nonparametric Bayesian generative model. Experiments on
three medium size data sets and the entire Wikipedia dataset show that HDTM can
infer accurate hierarchies even over large information networks.
