Graph Neural Networks for the Graphical Bootstrap

arXiv:2607.03109v1 Announce Type: cross Abstract: We study a graph classification problem involving over 20 million graphs, arising from high-order perturbative computations of correlators in planar 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.

Source: arXiv cs.LG | 2026-07-07