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arXiv · 2404.02692

Automated Inference of Graph Transformation Rules

Jakob L. Andersen·Akbar Davoodi·Rolf Fagerberg·Christoph Flamm·Walter Fontana·Juri Kolčák·Christophe V. F. P. Laurent·Daniel Merkle·Nikolai Nøjgaard
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Abstract

The explosion of data available in life sciences is fueling an increasing demand for expressive models and computational methods. Graph transformation is a model for dynamic systems with a large variety of applications. We introduce a novel method of the graph transformation model construction, combining generative and dynamical viewpoints to give a fully automated data-driven model inference method. The method takes the input dynamical properties, given as a "snapshot" of the dynamics encoded by explicit transitions, and constructs a compatible model. The obtained model is guaranteed to be minimal, thus framing the approach as model compression (from a set of transitions into a set of rules). The compression is permissive to a lossy case, where the constructed model is allowed to exhibit behavior outside of the input transitions, thus suggesting a completion of the input dynamics. The task of graph transformation model inference is naturally highly challenging due to the combinatorics involved. We tackle the exponential explosion by proposing a heuristically minimal translation of the task into a well-established problem, set cover, for which highly optimized solutions exist. We further showcase how our results relate to Kolmogorov complexity expressed in terms of graph transformation.

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BibTeXRIS

Jakob L. Andersen, Akbar Davoodi, Rolf Fagerberg, Christoph Flamm, Walter Fontana, Juri Kolčák, Christophe V. F. P. Laurent, Daniel Merkle, Nikolai Nøjgaard. 2024-04-03. Automated Inference of Graph Transformation Rules. https://doi.org/10.46298/fi.13347

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