Papers › GRPE: Relative Positional Encoding for Graph Transformer
GRPE: Relative Positional Encoding for Graph Transformer
Wonpyo Park, WoongGi Chang, Donggeon Lee, Juntae Kim, Seung-won Hwang
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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Code
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Code Syntology ran Syntology
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Regression | PCQM4Mv2-LSC | GRPE-Large | Test MAE | 0.0876 | #15 of 20 | Archive leaderboard | report |
| Graph Regression | PCQM4Mv2-LSC | GRPE-Large | Validation MAE | 0.0867 | #15 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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