  Measuring the impact of scientific articles is important for evaluating the
research output of individual scientists, academic institutions and journals.
While citations are raw data for constructing impact measures, there exist
biases and potential issues if factors affecting citation patterns are not
properly accounted for. In this work, we address the problem of field variation
and introduce an article level metric useful for evaluating individual
articles' visibility. This measure derives from joint probabilistic modeling of
the content in the articles and the citations amongst them using latent
Dirichlet allocation (LDA) and the mixed membership stochastic blockmodel
(MMSB). Our proposed model provides a visibility metric for individual articles
adjusted for field variation in citation rates, a structural understanding of
citation behavior in different fields, and article recommendations which take
into account article visibility and citation patterns. We develop an efficient
algorithm for model fitting using variational methods. To scale up to large
networks, we develop an online variant using stochastic gradient methods and
case-control likelihood approximation. We apply our methods to the benchmark
KDD Cup 2003 dataset with approximately 30,000 high energy physics papers.
