  Latent topic models have been successfully applied as an unsupervised topic
discovery technique in large document collections. With the proliferation of
hypertext document collection such as the Internet, there has also been great
interest in extending these approaches to hypertext [6, 9]. These approaches
typically model links in an analogous fashion to how they model words - the
document-link co-occurrence matrix is modeled in the same way that the
document-word co-occurrence matrix is modeled in standard topic models. In this
paper we present a probabilistic generative model for hypertext document
collections that explicitly models the generation of links. Specifically, links
from a word w to a document d depend directly on how frequent the topic of w is
in d, in addition to the in-degree of d. We show how to perform EM learning on
this model efficiently. By not modeling links as analogous to words, we end up
using far fewer free parameters and obtain better link prediction results.
