AI RESEARCH
A Spectral Framework for Graph Neural Operators: Convergence Guarantees and Tradeoffs
arXiv CS.LG
•
ArXi:2510.20954v3 Announce Type: replace-cross Graphons, as limits of graph sequences, provide an operator-theoretic framework for analyzing the asymptotic behavior of graph neural operators. Spectral convergence of sampled graphs to graphons induces convergence of the corresponding neural operators, enabling transferability analyses of graph neural networks (GNNs