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...
Autors principals: | He, Y, Gan, Q, Wipf, D, Reinert, G, Yan, J, Cucuringu, M |
---|---|
Format: | Conference item |
Idioma: | English |
Publicat: |
Journal of Machine Learning Research
2022
|
Ítems similars
-
Ranking and synchronization from pairwise measurements via SVD
per: d'Aspremont, A, et al.
Publicat: (2021) -
Rank Centrality: Ranking from Pairwise Comparisons
per: Negahban, Sahand, et al.
Publicat: (2017) -
Sync-Rank: Robust ranking, constrained ranking and rank aggregation via eigenvector and SDP synchronization
per: Cucuringu, M
Publicat: (2016) -
MSGNN: a spectral graph neural network based on a novel magnetic signed Laplacian
per: He, Y, et al.
Publicat: (2022) -
Pairwise diffusion of preference rankings in social networks
per: Brill, M, et al.
Publicat: (2016)