GNNRank: learning global rankings from pairwise comparisons via directed graph neural networks

GNNRank: learning global rankings from pairwise comparisons via directed graph neural networks

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...

Повний опис

Бібліографічні деталі
Автори: He, Y, Gan, Q, Wipf, D, Reinert, G, Yan, J, Cucuringu, M
Формат: Conference item
Мова:English
Опубліковано: Journal of Machine Learning Research 2022

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