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Directed Graph Grammars for Sequence-based Learning

arXiv 2505.22949 ↗First posted 29 May 2025cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 76 references that are in the map. First posted on arXiv 29 May 2025.

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References · 30

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18 more
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  18. 2020Directed Graph Hashingcited by 2

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