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Recovering global rankings from pairwise comparisons has wide applicationsfrom time synchronization to sports team ranking. Pairwise comparisonscorresponding to matches in a competition can be construed as edges in adirected graph (digraph), whose nodes represent e.g. competitors with anunknown rank. In this paper, we introduce neural networks into the rankingrecovery problem by proposing the so-called GNNRank, a trainable GNN-basedframework with digraph embedding. Moreover, new objectives are devised toencode ranking upsets/violations. The framework involves a ranking scoreestimation approach, and adds an inductive bias by unfolding the Fiedler vectorcomputation of the graph constructed from a learnable similarity matrix.Experimental results on extensive data sets show that our methods attaincompetitive and often superior performance against baselines, as well asshowing promising transfer ability. Codes and preprocessed data are at:\url{https://github.com/SherylHYX/GNNRank}.

GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks