Automated Inference of Graph Transformation Rules
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915069547249664 |
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| author | Andersen, Jakob L. Davoodi, Akbar Fagerberg, Rolf Flamm, Christoph Fontana, Walter Kolčák, Juri Laurent, Christophe V. F. P. Merkle, Daniel Nøjgaard, Nikolai |
| author_facet | Andersen, Jakob L. Davoodi, Akbar Fagerberg, Rolf Flamm, Christoph Fontana, Walter Kolčák, Juri Laurent, Christophe V. F. P. Merkle, Daniel Nøjgaard, Nikolai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Inference of Graph Transformation Rules Andersen, Jakob L. Davoodi, Akbar Fagerberg, Rolf Flamm, Christoph Fontana, Walter Kolčák, Juri Laurent, Christophe V. F. P. Merkle, Daniel Nøjgaard, Nikolai Discrete Mathematics Machine Learning Molecular Networks 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. |
| title | Automated Inference of Graph Transformation Rules |
| topic | Discrete Mathematics Machine Learning Molecular Networks |
| url | https://arxiv.org/abs/2404.02692 |