Web: http://arxiv.org/abs/2202.00211

June 16, 2022, 1:12 a.m. | Yixuan He, Quan Gan, David Wipf, Gesine Reinert, Junchi Yan, Mihai Cucuringu

stat.ML updates on arXiv.org arxiv.org

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 …

arxiv global graph graph neural networks learning lg networks neural neural networks

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