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Graph Transformer

298 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

This is Graph Transformer method, proposed as a generalization of Transformer Neural Network architectures, for arbitrary graphs.

Compared to the original Transformer, the highlights of the presented architecture are:

Source: A Generalization of Transformer Networks to GraphsSee Code · graphdeeplearning/graphtransformer

Papers archive 2025-07-28

30 shown of 298, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 276 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Representation Learning54
Node Classification38
Graph Learning37
Graph Representation Learning28
Graph Neural Network24
Prediction19
Graph Regression18
Graph Classification17
Link Prediction17
Property Prediction13
Contrastive Learning12
Drug Discovery12
Graph Attention12
Graph Generation12
Molecular Property Prediction12
Recommendation Systems11
Denoising10
Decoder9
Self-Supervised Learning9
Language Modelling8

Usage over time archive 2025-07-28

Papers per year tagged with Graph Transformer: 2020 to 2025, peak 100 100 0 2020: 2 papers 2020 2021: 26 papers 2021 2022: 44 papers 2022 2023: 87 papers 2023 2024: 100 papers 2024 2025: 39 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (298 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Graph Models

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