GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks. (arXiv:2202.00211v2 [cs.LG] UPDATED)

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

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