  In recent years, many studies have been focusing on predicting the scientific
impact of research papers. Most of these predictions are based on citations
count or rely on features obtainable only from already published papers. In
this study, we predict the likelihood for a research paper of winning an award
only relying on information available at publication time. For each paper, we
build the citation subgraph induced from its bibliography. We initially
consider some features of this subgraph, such as the density and the global
clustering coefficient, to make our prediction. Then, we mix this information
with textual features, extracted from the abstract and the title, to obtain a
more accurate final prediction. We made our experiments considering the
ArnetMiner citation graph, while the ground truth on award-winning papers has
been obtained from a collection of best paper awards from 32 computer science
conferences. In our experiment, we obtained an encouraging F1 score of 0.694.
Remarkably, The high recall and the low false negatives rate, show how the
model performs very well at identifying papers that will not win an award. This
behavior can help researchers in getting a first evaluation of their work at
publication time. Lastly, we made some first experiments on interpretability.
Our results highlight some interesting patterns both in topological and textual
features.
