Directed Graph Grammars for Sequence-based Learning

• URL: http://arxiv.org/abs/2505.22949v1
• Date: Thu, 29 May 2025 00:05:07 GMT
• Title: Directed Graph Grammars for Sequence-based Learning
• Authors: Michael Sun, Orion Foo, Gang Liu, Wojciech Matusik, Jie Chen,
• Abstract summary: directed acyclic graphs (DAGs) are a class of graphs commonly used in practice.<n>We propose a grammar-based approach to constructing a principled, compact and equivalent sequential representation of a DAG.
• Score: 20.579076532082684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Directed acyclic graphs (DAGs) are a class of graphs commonly used in practice, with examples that include electronic circuits, Bayesian networks, and neural architectures. While many effective encoders exist for DAGs, it remains challenging to decode them in a principled manner, because the nodes of a DAG can have many different topological orders. In this work, we propose a grammar-based approach to constructing a principled, compact and equivalent sequential representation of a DAG. Specifically, we view a graph as derivations over an unambiguous grammar, where the DAG corresponds to a unique sequence of production rules. Equivalently, the procedure to construct such a description can be viewed as a lossless compression of the data. Such a representation has many uses, including building a generative model for graph generation, learning a latent space for property prediction, and leveraging the sequence representational continuity for Bayesian Optimization over structured data. Code is available at https://github.com/shiningsunnyday/induction.

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