  This paper addresses the problem of community detection in networked data
that combines link and content analysis. Most existing work combines link and
content information by a generative model. There are two major shortcomings
with the existing approaches. First, they assume that the probability of
creating a link between two nodes is determined only by the community
memberships of the nodes; however other factors (e.g. popularity) could also
affect the link pattern. Second, they use generative models to model the
content of individual nodes, whereas these generative models are vulnerable to
the content attributes that are irrelevant to communities. We propose a
Bayesian framework for combining link and content information for community
detection that explicitly addresses these shortcomings. A new link model is
presented that introduces a random variable to capture the node popularity when
deciding the link between two nodes; a discriminative model is used to
determine the community membership of a node by its content. An approximate
inference algorithm is presented for efficient Bayesian inference. Our
empirical study shows that the proposed framework outperforms several
state-of-theart approaches in combining link and content information for
community detection.
