Graph Grammar

Graph grammars offer a powerful framework for representing and generating structured data, particularly graphs, by defining rules for manipulating graph structures. Current research focuses on developing and applying graph grammars in diverse areas, including image analysis (using hierarchical grammars and recurrent neural networks to model image semantics and syntax), molecular design (leveraging learnable grammars for data-efficient property prediction and generation), and dynamic systems modeling (through extensions like dynamic vertex-replacement grammars). These advancements enable more efficient and interpretable modeling of complex systems, leading to improved performance in tasks ranging from image corruption detection to molecular property prediction and data generation.