Unsupervised Induction of Neural Graph Grammars

Probabilistic graph grammar provide a principled framework for modeling graphs through latent compositional processes, yet existing approaches have largely remained confined to statistical models and linguistic applications. We introduce neural graph grammars, establishing a flexible neural parameteriztaion while preserving exact inference over latent derivations. We show that structural encoding improves generalization through parameter sharing across structurally related rules. We further demonstrate the applicability of learned graph grammars beyond language, using them for graph generation and molecular property prediction. Together, these result show neural graph grammars as a compositional model for learning, generating, and analyzing graphs across domains.