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arXiv · 2609.13844

Pre-training with Graph Transformers

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.

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Jiaming Wang, Thomas Laurent, Xavier Bresson. 2026-09-12. Pre-training with Graph Transformers. https://arxiv.org/abs/2609.13844

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