  Estimating the expected impact of an article is valuable for various
applications (e.g., article/cooperator recommendation). Most existing
approaches attempt to predict the exact number of citations each article will
receive in the near future, however this is a difficult regression analysis
problem. Moreover, most approaches rely on the existence of rich metadata for
each article, a requirement that cannot be adequately fulfilled for a large
number of them. In this work, we take advantage of the fact that solving a
simpler machine learning problem, that of classifying articles based on their
expected impact, is adequate for many real world applications and we propose a
simplified model that can be trained using minimal article metadata. Finally,
we examine various configurations of this model and evaluate their
effectiveness in solving the aforementioned classification problem.
