Graph Neural Networks for the Graphical Bootstrap

• URL: http://arxiv.org/abs/2607.03109v1
• Date: Fri, 03 Jul 2026 08:45:19 GMT
• Title: Graph Neural Networks for the Graphical Bootstrap
• Abstract summary: We benchmark graph neural networks, including graph transformers, achieving robust generalization to larger graphs with up to $99.996%$ ROC AUC.<n>We analyze how the models can be used to gain a computational speedup compared to the traditional graphical bootstrap algorithm, through shrinking the redundant data by up to $85.5%$ at the level of denominator graphs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study a graph classification problem involving over 20 million graphs, arising from high-order perturbative computations of correlators in planar $\mathcal{N}=4$ super-Yang--Mills, a model closely related to the theory of the strong nuclear force. We benchmark graph neural networks, including graph transformers, achieving robust generalization to larger graphs with up to $99.996\%$ ROC AUC. Then, we analyze how the models can be used to gain a computational speedup compared to the traditional graphical bootstrap algorithm, through shrinking the redundant data by up to $85.5\%$ at the level of denominator graphs. Finally, we study the embeddings of the models to investigate their interpretability.

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