Context-free graph grammars have shown a remarkable ability to model structures in real-world relational data. However, graph grammars lack the ability to capture time-changing phenomena since the left-to-right transitions of a production rule do not represent temporal change. In the present work, we describe dynamic vertex-replacement grammars (DyVeRG), which generalize vertex replacement grammars in the time domain by providing a formal framework for updating a learned graph grammar in accordance with modifications to its underlying data. We show that DyVeRG grammars can be learned from, and used to generate, real-world dynamic graphs faithfully while remaining human-interpretable. We also demonstrate their ability to forecast by computing dyvergence scores, a novel graph similarity measurement exposed by this framework.
@article{arxiv.2303.11553,
title = {Dynamic Vertex Replacement Grammars},
author = {Daniel Gonzalez Cedre and Justus Isaiah Hibshman and Timothy La Fond and Grant Boquet and Tim Weninger},
journal= {arXiv preprint arXiv:2303.11553},
year = {2023}
}