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...
| Главные авторы: | , , , , , |
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| Формат: | Conference item |
| Язык: | English |
| Опубликовано: |
Journal of Machine Learning Research
2022
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