Pre-training with Graph Transformers

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.

4 pages, 1 table. DLG-KDD 2023 workshop paper

Machine Learning (cs.LG)

arXiv:2609.13844 [cs.LG]

(or arXiv:2609.13844v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.13844 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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