Feel free to choose any method to calculate mincuts computationally from graphs. I list below a simple example, relevant research, models, storage methods, lemmas, theorems -- and something about visualisation and computing on which this thread is focused on. Next step after the simple example is the parametrisation of the graphical model and then computation.
Simple example with Python and Cartesian product
Assume a 3x2x2 graph with 3 parallel where 1st branch with 3 things, 2nd branch with 2 things and last branch 2 things. The minimum cuts are {{1,4,6},{1,4,7},{1,5,6},{1,5,7},{2,4,6},{2,4,7},{2,5,6},{2,5,7},{3,4,6},{3,4,7},{3,5,6},{3,5,7}}.

import itertools;
somelists = [
[1, 2, 3],
[4, 5],
[6, 7]
]
print list(itertools.product(*somelists))
Computing
General question How to analyse a sparse adjacency matrix? but relevant because graphs often are sparse.
Graph Complement useful in finding mincuts: a trial with Mathematica below but notice that some bugs in Mathematica found in at least 10.1 related to mincut and VertexConnectivity commands.
Mathematics
Visualising series-parallel graphs with sink and source