Directed Graph Grammars for Sequence-based Learning

This work introduces a grammar-based method to represent directed acyclic graphs (DAGs) as unique, sequential derivations over an unambiguous grammar, enabling lossless compression and principled decoding. This compact representation facilitates applications such as graph generation, property prediction, and Bayesian optimization by providing a continuous sequence-based structure for complex DAGs.