  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.
