Graph Neural Networks for the Graphical Bootstrap
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 interp...
| Authors | Rigers Aliaj, Gabriele Dian, Reza Doobary et al. |
|---|---|
| Primary category | hep-th |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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