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...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
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Journal of Machine Learning Research
2022
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| _version_ | 1797109563670921216 |
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| author | He, Y Gan, Q Wipf, D Reinert, G Yan, J Cucuringu, M |
| author_facet | He, Y Gan, Q Wipf, D Reinert, G Yan, J Cucuringu, M |
| author_sort | He, Y |
| collection | OXFORD |
| description | 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}.
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| first_indexed | 2024-03-07T07:43:29Z |
| format | Conference item |
| id | oxford-uuid:23283d2e-dfeb-4fd8-86d5-148dd945d7a0 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:43:29Z |
| publishDate | 2022 |
| publisher | Journal of Machine Learning Research |
| record_format | dspace |
| spelling | oxford-uuid:23283d2e-dfeb-4fd8-86d5-148dd945d7a02023-05-17T16:22:59ZGNNRank: learning global rankings from pairwise comparisons via directed graph neural networksConference itemhttp://purl.org/coar/resource_type/c_5794uuid:23283d2e-dfeb-4fd8-86d5-148dd945d7a0EnglishSymplectic ElementsJournal of Machine Learning Research2022He, YGan, QWipf, DReinert, GYan, JCucuringu, MRecovering 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}. |
| spellingShingle | He, Y Gan, Q Wipf, D Reinert, G Yan, J Cucuringu, M GNNRank: learning global rankings from pairwise comparisons via directed graph neural networks |
| title | GNNRank: learning global rankings from pairwise comparisons via directed graph neural networks |
| title_full | GNNRank: learning global rankings from pairwise comparisons via directed graph neural networks |
| title_fullStr | GNNRank: learning global rankings from pairwise comparisons via directed graph neural networks |
| title_full_unstemmed | GNNRank: learning global rankings from pairwise comparisons via directed graph neural networks |
| title_short | GNNRank: learning global rankings from pairwise comparisons via directed graph neural networks |
| title_sort | gnnrank learning global rankings from pairwise comparisons via directed graph neural networks |
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