GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed
Graph Neural Networks
- URL: http://arxiv.org/abs/2202.00211v1
- Date: Tue, 1 Feb 2022 04:19:50 GMT
- Title: GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed
Graph Neural Networks
- Authors: Yixuan He and Quan Gan and David Wipf and Gesine Reinert and Junchi
Yan and Mihai Cucuringu
- Abstract summary: We introduce GNNRank, a modeling framework compatible with any GNN capable of learning digraph embeddings.
We show that our methods attain competitive and often superior performance compared with existing approaches.
- Score: 68.61934077627085
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recovering global rankings from pairwise comparisons is an important problem
with many applications, ranging from time synchronization to sports team
ranking. Pairwise comparisons corresponding to matches in a competition can
naturally be construed as edges in a directed graph (digraph), whose nodes
represent competitors with an unknown rank or skill strength. However, existing
methods addressing the rank estimation problem have thus far not utilized
powerful neural network architectures to optimize ranking objectives. Hence, we
propose to augment an algorithm with neural network, in particular graph neural
network (GNN) for its coherence to the problem at hand. In this paper, we
introduce GNNRank, a modeling framework that is compatible with any GNN capable
of learning digraph embeddings, and we devise trainable objectives to encode
ranking upsets/violations. This framework includes a ranking score estimation
approach, and adds a useful inductive bias by unfolding the Fiedler vector
computation of the graph constructed from a learnable similarity matrix.
Experimental results on a wide range of data sets show that our methods attain
competitive and often superior performance compared with existing approaches.
It also shows promising transfer ability to new data based on the trained GNN
model.
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