  In this paper we introduce a new approach to computing hidden features of
sampled vector fields. The basic idea is to convert the vector field data to a
graph structure and use tools designed for automatic, unsupervised analysis of
graphs. Using a few data sets we show that the collected features of the vector
fields are correlated with the dynamics known for analytic models which
generates the data. In particular the method may be useful in analysis of data
sets where the analytic model is poorly understood or not known.
