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.

介绍一种结合生成和动态观点的图变换模型构建方法，通过对输入的动态属性进行压缩构建兼容模型，该方法采用一种启发式的方法将图变换模型推演问题转化为已知问题的集合覆盖问题。