  Graph neural networks (GNNs) have been successfully applied in many
structured data domains, with applications ranging from molecular property
prediction to the analysis of social networks. Motivated by the broad
applicability of GNNs, we propose the family of so-called RankGNNs, a
combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are
trained with a set of pair-wise preferences between graphs, suggesting that one
of them is preferred over the other. One practical application of this problem
is drug screening, where an expert wants to find the most promising molecules
in a large collection of drug candidates. We empirically demonstrate that our
proposed pair-wise RankGNN approach either significantly outperforms or at
least matches the ranking performance of the naive point-wise baseline
approach, in which the LtR problem is solved via GNN-based graph regression.
