BoGrape: Bayesian optimization over graphs with shortest-path encoded
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914493713350656 |
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| author | Xie, Yilin Zhang, Shiqiang Qing, Jixiang Misener, Ruth Tsay, Calvin |
| author_facet | Xie, Yilin Zhang, Shiqiang Qing, Jixiang Misener, Ruth Tsay, Calvin |
| contents | Graph-structured data are central to many scientific and industrial applications where the goal is to optimize expensive black-box objectives defined over graph structures or node configurations -- as seen in molecular design, supply chains, and sensor placement. Bayesian optimization offers a principled approach for such settings, but existing methods largely focus on functions defined over nodes of a fixed graph. Moreover, graph optimization is often approached heuristically, and it remains unclear how to systematically incorporate structural constraints into BO. To address these gaps, we build on shortest-path graph kernels to develop a principled framework for acquisition optimization over unseen graph structures and associated node attributes. Through a novel formulation based on mixed-integer programming, we enable global exploration of the combinatorial domain over graph structures and explicit embedding of problem-specific constraints. We demonstrate that our method, BoGrape, is competitive both on general synthetic benchmarks and representative molecular design case studies with application-specific constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BoGrape: Bayesian optimization over graphs with shortest-path encoded Xie, Yilin Zhang, Shiqiang Qing, Jixiang Misener, Ruth Tsay, Calvin Optimization and Control Graph-structured data are central to many scientific and industrial applications where the goal is to optimize expensive black-box objectives defined over graph structures or node configurations -- as seen in molecular design, supply chains, and sensor placement. Bayesian optimization offers a principled approach for such settings, but existing methods largely focus on functions defined over nodes of a fixed graph. Moreover, graph optimization is often approached heuristically, and it remains unclear how to systematically incorporate structural constraints into BO. To address these gaps, we build on shortest-path graph kernels to develop a principled framework for acquisition optimization over unseen graph structures and associated node attributes. Through a novel formulation based on mixed-integer programming, we enable global exploration of the combinatorial domain over graph structures and explicit embedding of problem-specific constraints. We demonstrate that our method, BoGrape, is competitive both on general synthetic benchmarks and representative molecular design case studies with application-specific constraints. |
| title | BoGrape: Bayesian optimization over graphs with shortest-path encoded |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2503.05642 |