Parallel Noising in Neural Markov Logic Networks

Abstract:Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model. Previous work has shown that, although NMLNs achieve strong performance as generative models for small relational structures, they underperform diffusion-based generative graph models on larger structures. In this paper, we strengthen NMLNs along two main dimensions: (i) we increase the expressive capacity of their potential functions using graph neural networks, and (ii) we develop a new training and inference algorithm inspired by parallel-tempering Markov chain Monte Carlo methods, which we name parallel noising. Together, these enhancements enable NMLNs to attain strong performance in graph generation relative to general diffusion-based generative graph models. Furthermore, they allow NMLNs to match the performance of specialized text-based recurrent models when generating small molecular structures.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.19126 [cs.LG]

(or arXiv:2607.19126v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.19126 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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