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digraph  {
all_pairs_shortest_path_length;
boruvka_mst_edges;
greedy_k_edge_augmentation;
minimum_cut_value;
immediate_dominators;
closeness_centrality;
dag_longest_path;
condensation;
maximum_flow_value;

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# It is nice to experiment in a Jupyter Notebook
import numpy as np
import scipy as sp
import drawsvg as draw

gcube = np.array([
    [-1, 1, -1],
    [-1, 1, 1],
    [1, 1, 1],
    [-1, -1, 1],

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import sys
import networkx as nx
from collections import defaultdict
from types import FunctionType


def fullname(func):
    return f"{func.__module__}.{func.__name__}"

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Copyright (c) 2018 Erik Welch

All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

  a. Redistributions of source code must retain the above copyright notice,
     this list of conditions and the following disclaimer.
  b. Redistributions in binary form must reproduce the above copyright

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