GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
This addresses ranking recovery for applications like sports team ranking, but it is incremental as it builds on existing GNN and ranking methods.
The paper tackles the problem of recovering global rankings from pairwise comparisons by introducing GNNRank, a trainable graph neural network framework with digraph embedding and new objectives for ranking violations, achieving competitive and often superior performance on extensive datasets.
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}.