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.

翻译：生命科学领域可用数据的爆炸式增长正推动着对表达性模型与计算方法的日益增长的需求。图变换是一种动态系统模型，具有广泛的应用。我们提出了一种新颖的图变换模型构建方法，该方法结合了生成性与动态性视角，给出了一种完全自动化的数据驱动模型推断方法。该方法以动态属性作为输入（这些属性由显式转换编码的动力学"快照"给出），并构建一个兼容的模型。所获得的模型保证是最小的，从而将该方法框定为模型压缩（从一组转换压缩为一组规则）。该压缩允许有损情况，即允许构建的模型表现出输入转换之外的行为，从而暗示了对输入动力学的补全。由于涉及组合数学，图变换模型推断任务天然极具挑战性。我们通过将该任务启发式地最小化翻译为一个已有完善解决方案的经典问题——集合覆盖问题，来应对指数爆炸。我们进一步展示了我们的结果如何与用图变换表示的柯尔莫哥洛夫复杂度相关联。