Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representations

Excerpt

arXiv:2511.07734v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discrete and combinatorial nature of graphs. Existing approaches often rely either on full graph topology, which is impractical for large or partially observed graphs, or on incremental local exploration, which can lead to slow convergence. We introduce a scalable fra