# Analyzing B"uchi Automata with Graph Neural Networks

arXiv: https://arxiv.org/abs/2206.09619 · HTML: https://mlsanity.com/paper/2206.09619/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Can Graph Neural Networks Learn to Solve MaxSAT Problem?](https://mlsanity.com/paper/2111.07568/) (2021)

## Built on by (most cited first, 1 of 1)

- [RNN Generalization to Omega-Regular Languages](https://mlsanity.com/paper/2509.02491/) (2025)

## Abstract (arXiv)

B"uchi Automata on infinite words present many interesting problems and are used frequently in program verification and model checking. A lot of these problems on B"uchi automata are computationally hard, raising the question if a learning-based data-driven analysis might be more efficient than using traditional algorithms. Since B"uchi automata can be represented by graphs, graph neural networks are a natural choice for such a learning-based analysis. In this paper, we demonstrate how graph neural networks can be used to reliably predict basic properties of B"uchi automata when trained on automatically generated random automata datasets.