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GRPE: Relative Positional Encoding for Graph Transformer

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arxiv 2201.12787 v3 pith:763LZF6W submitted 2022-01-30 cs.LG cs.AI

Wonpyo Park , Woonggi Chang , Donggeon Lee , Juntae Kim , Seung-won Hwang This is my paper
classification cs.LGcs.AI
keywords graphrelativeapproachesencodingmethodpositionpositionalencode
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We propose a novel positional encoding for learning graph on Transformer architecture. Existing approaches either linearize a graph to encode absolute position in the sequence of nodes, or encode relative position with another node using bias terms. The former loses preciseness of relative position from linearization, while the latter loses a tight integration of node-edge and node-topology interaction. To overcome the weakness of the previous approaches, our method encodes a graph without linearization and considers both node-topology and node-edge interaction. We name our method Graph Relative Positional Encoding dedicated to graph representation learning. Experiments conducted on various graph datasets show that the proposed method outperforms previous approaches significantly. Our code is publicly available at https://github.com/lenscloth/GRPE.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Logarithmic High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback

    cs.LG 2026-03 unverdicted novelty 8.0 of 10

    Standard two-point projected gradient achieves fixed-comparator high-probability logarithmic regret for strongly convex OCO with two-point bandit feedback, with a leading d (not d²) horizon term.

  2. Multi-Embodiment Robotic Retargeting via Guided Diffusion Model

    cs.RO 2025-05 reject novelty 5.0 of 10

    A graph-conditioned diffusion model retargets motions across heterogeneous robot embodiments without needing target-robot motion data, yet lacks baseline comparisons and error bars in its validation.

  3. On Preserving Geometrical Invariance for Superpixel Image Classification using Graph Transformer

    cs.LG 2026-07 conditional novelty 4.5 of 10

    A GraphGPS-style transformer on SLIC RAGs with mean-centered centroids reaches ~80.2% CIFAR-10 accuracy, matching ShapeGNN without boundary-point features and with better low-data stability.

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