论文标题
Gnnrank：通过有向图神经网络从成对比较中学习全球排名
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
论文作者
论文摘要
从成对比较中恢复全球排名从时间同步到运动队排名的广泛应用。对应于竞争中匹配的成对比较可以解释为有向图（Digraph）中的边缘，其节点代表例如竞争对手的排名未知。在本文中，我们通过提出所谓的Gnnrank，这是一种基于Digraph嵌入的基于训练的GNN框架，将神经网络引入排名恢复问题。此外，设计了新的目标来编码排名upsess/违规行为。该框架涉及一种排名得分估计方法，并通过展开从可学习相似性矩阵构建的图表的fiedler矢量计算来增加电感偏差。广泛的数据集的实验结果表明，我们的方法具有竞争性，并且通常针对基准的表现以及表现出有希望的转移能力。代码和预处理数据为：\ url {https://github.com/sherylyx/gnnrank}。
Recovering global rankings from pairwise comparisons has wide applications from time synchronization to sports team ranking. Pairwise comparisons corresponding to matches in a competition can be construed as edges in a directed graph (digraph), whose nodes represent e.g. competitors with an unknown rank. In this paper, we introduce neural networks into the ranking recovery problem by proposing the so-called GNNRank, a trainable GNN-based framework with digraph embedding. Moreover, new objectives are devised to encode ranking upsets/violations. The framework involves a ranking score estimation approach, and adds an inductive bias by unfolding the Fiedler vector computation of the graph constructed from a learnable similarity matrix. Experimental results on extensive data sets show that our methods attain competitive and often superior performance against baselines, as well as showing promising transfer ability. Codes and preprocessed data are at: \url{https://github.com/SherylHYX/GNNRank}.
