arXiv — Machine Learning · September 15, 2026 · 2 min read

Pre-training with Graph Transformers

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Computer Science > Machine Learning

arXiv:2609.13844 (cs)

[Submitted on 12 Sep 2026]

Title:Pre-training with Graph Transformers

Authors:Jiaming Wang, Thomas Laurent, Xavier Bresson

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Abstract:This article investigates pre-training strategies for graph transformers in the biochemistry domain. By conducting comprehensive experiments, the study reveals that supervised pre-training using computed properties as labels provides the highest performance gain on downstream tasks. The results also highlight the importance of constraining model capacity to mitigate overfitting in graph transformers.

Comments:	4 pages, 1 table. DLG-KDD 2023 workshop paper
Subjects:	Machine Learning (cs.LG)
Cite as:	arXiv:2609.13844 [cs.LG]
	(or arXiv:2609.13844v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2609.13844 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jiaming Wang [view email]
[v1] Sat, 12 Sep 2026 10:01:03 UTC (10 KB)

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