  Academic citation graphs represent citation relationships between
publications across the full range of academic fields. Top cited papers
typically reveal future trends in their corresponding domains which is of
importance to both researchers and practitioners. Prior citation prediction
methods often require initial citation trends to be established and do not take
advantage of the recent advancements in graph neural networks (GNNs). We
present GNN-based architecture that predicts the top set of papers at the time
of publication. For experiments, we curate a set of academic citation graphs
for a variety of conferences and show that the proposed model outperforms other
classic machine learning models in terms of the F1-score.
