Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, probabilistic inference is particularly valuable, as it enables composable generation and principled incorporation of desired constraints, such as structural or functional properties. Energy-based models naturally support this goal by capturing relative likelihoods and enabling composable inference by directly enforcing constraints during inference. However, discrete energy-based models typically struggle with efficient and high-quality sampling, as off-support regions often contain spurious local minima, trappin...
| Authors | Michal Balcerak, Suprosanna Shit, Chinmay Prabhakar et al. |
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| Primary category | cs.LG |
| Published | 24 March 2026 |
| Last updated at source | 1 June 2026 |
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