  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.
