  Document networks are found in various collections of real-world data, such
as citation networks, hyperlinked web pages, and online social networks. A
large number of generative models have been proposed because they offer
intuitive and useful pictures for analyzing document networks. Prominent
examples are relational topic models, where documents are linked according to
their topic similarities. However, existing generative models do not make full
use of network structures because they are largely dependent on topic modeling
of documents. In particular, centrality of graph nodes is missing in generative
processes of previous models. In this paper, we propose a novel generative
model for document networks by introducing random walkers on networks to
integrate the node centrality into link generation processes. The developed
method is evaluated in semi-supervised classification tasks with real-world
citation networks. We show that the proposed model outperforms existing
probabilistic approaches especially in detecting communities in connected
networks.
