  Re-ranking systems aim to reorder an initial list of documents to satisfy
better the information needs associated with a user-provided query. Modern
re-rankers predominantly rely on neural network models, which have proven
highly effective in representing samples from various modalities. However,
these models typically evaluate query-document pairs in isolation, neglecting
the underlying document distribution that could enhance the quality of the
re-ranked list. To address this limitation, we propose Graph Neural Re-Ranking
(GNRR), a pipeline based on Graph Neural Networks (GNNs), that enables each
query to consider documents distribution during inference. Our approach models
document relationships through corpus subgraphs and encodes their
representations using GNNs. Through extensive experiments, we demonstrate that
GNNs effectively capture cross-document interactions, improving performance on
popular ranking metrics. In TREC-DL19, we observe a relative improvement of
5.8% in Average Precision compared to our baseline. These findings suggest that
integrating the GNN segment offers significant advantages, especially in
scenarios where understanding the broader context of documents is crucial.
