def test_make_small_graph(self):
d = [
"adjacencylist", "Bull Graph", 5,
[[2, 3], [1, 3, 4], [1, 2, 5], [2], [3]]
]
G = nx.make_small_graph(d)
assert is_isomorphic(G, nx.bull_graph())
def get_matplotlib_object_that_represents_graph_edges():
'''
networkx.drawing.nx_pylab.draw_networkx_edges() returns a matplotlib.collection.LineCollection
- A graph object and a dictionary of positions are both required arguments to draw_networkx_edges()
- Optionally, I can provide a list of edges in which case only the provided edges will be drawn
- Version incompatibilities:
- networkx==1.11 and matplotlib==2.2.5 draws fine
- The LineCollection is sneakily autoscaled! This is not standard in matplotlib
- networkx==1.11 and matplotlib==3.1.3 fails to run successfully
- networkx==2.4 and matplotlib==2.2.5 draws fine
- Need to invoke autoscale()
- networkx==2.4 and matplotlib==3.1.3 draws fine
- Need to invoke autoscale()
'''
fig, ax = plt.subplots() # Works fine
g = nx.bull_graph()
pos = nx_agraph.graphviz_layout(g)
print(g.edges()) # [(0, 1), (0, 2), (1, 2), (1, 3), (2, 4)]
lc = nx.drawing.nx_pylab.draw_networkx_edges(g, pos)
#lc = nx.drawing.nx_pylab.draw_networkx_edges(g, pos, edgelist=[(0, 1)])
#fig, ax = plt.subplots() # Causes RuntimeError
lc.set_color(('r', 'g', 'b', 'y', 'c', 'm', 'k'))
ax.add_collection(lc)
ax.autoscale()
plt.show()
def ego_graphs():
print("Ego graphs")
# G = nx.star_graph(7)
G = nx.bull_graph()
EG = nx.ego_graph(G, 2)
draw_graph(G)
draw_graph(EG)
def test170_bullgraph(self):
""" Bull graph. """
g = nx.bull_graph()
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
#td.showGraph(g, mate1, "test170_bullgraph")
self.assertEqual( len(mate1), len(mate2) )
def test_complement():
null = nx.null_graph()
empty1 = nx.empty_graph(1)
empty10 = nx.empty_graph(10)
K3 = nx.complete_graph(3)
K5 = nx.complete_graph(5)
K10 = nx.complete_graph(10)
P2 = nx.path_graph(2)
P3 = nx.path_graph(3)
P5 = nx.path_graph(5)
P10 = nx.path_graph(10)
# complement of the complete graph is empty
G = nx.complement(K3)
assert nx.is_isomorphic(G, nx.empty_graph(3))
G = nx.complement(K5)
assert nx.is_isomorphic(G, nx.empty_graph(5))
# for any G, G=complement(complement(G))
P3cc = nx.complement(nx.complement(P3))
assert nx.is_isomorphic(P3, P3cc)
nullcc = nx.complement(nx.complement(null))
assert nx.is_isomorphic(null, nullcc)
b = nx.bull_graph()
bcc = nx.complement(nx.complement(b))
assert nx.is_isomorphic(b, bcc)
def test_make_small_graph(self):
d = ["adjacencylist", "Bull Graph", 5, [[2, 3], [1, 3, 4], [1, 2, 5], [2], [3]]]
G = nx.make_small_graph(d)
assert is_isomorphic(G, nx.bull_graph())
# Test small graph creation error with wrong ltype
d[0] = "erroneouslist"
pytest.raises(nx.NetworkXError, nx.make_small_graph, graph_description=d)
def test_bull(self):
expected = True
g = nx.bull_graph()
for o in enumerate_dfs_ordering_naively(g):
@spec_order(o)
def func(g):
return is_planar(g)
actual = func(g)
self.assertEqual(expected, actual)
def network_graph(df):
result = pd.read_csv(
'C:/Harshad.Ambekar/personal/github/hackathon-intel-auto/dataset/labels.csv'
)
G = nx.bull_graph()
pos = nx.spring_layout(G) # positions for all nodes
# nodes
nx.draw_networkx_nodes(
G,
pos,
#nodelist=['loginflow','tenantflow','authenflow','authflow'],
nodelist=[0, 1, 2, 3, 4],
node_color='b',
node_size=250,
alpha=0.8)
# edges
nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)
nx.draw_networkx_edges(
G,
pos,
edgelist=[(0, 1), (0, 2), (0, 3), (1, 2), (2, 4)],
#edgelist=[('loginflow','tenantflow'),('loginflow','authenflow'),('loginflow','authflow')],
width=4,
alpha=0.5,
edge_color='r')
# some math labels
labels = {}
labels[0] = r'$loginflow$'
labels[1] = r'$tenantflow$'
labels[2] = r'$authenflow$'
labels[3] = r'$authflow$'
labels[4] = r'$usercreation$'
nx.draw_networkx_labels(G, pos, labels, font_size=8)
#plt.axis('off')
#plt.savefig("labels_and_colors.png") # save as png
#plt.figure(figsize=(4, 3), dpi=70)
st.pyplot()
def test_complete_to_chordal_graph(self):
fgrg = nx.fast_gnp_random_graph
test_graphs = [
nx.barbell_graph(6, 2),
nx.cycle_graph(15),
nx.wheel_graph(20),
nx.grid_graph([10, 4]),
nx.ladder_graph(15),
nx.star_graph(5),
nx.bull_graph(),
fgrg(20, 0.3, seed=1),
]
for G in test_graphs:
H, a = nx.complete_to_chordal_graph(G)
assert nx.is_chordal(H)
assert len(a) == H.number_of_nodes()
if nx.is_chordal(G):
assert G.number_of_edges() == H.number_of_edges()
assert set(a.values()) == {0}
else:
assert len(set(a.values())) == H.number_of_nodes()
def BullGraph():
r"""
Returns a bull graph with 5 nodes.
A bull graph is named for its shape. It's a triangle with horns.
This constructor depends on `NetworkX <http://networkx.lanl.gov>`_
numeric labeling. For more information, see this
:wikipedia:`Wikipedia article on the bull graph <Bull_graph>`.
PLOTTING:
Upon construction, the position dictionary is filled to
override the spring-layout algorithm. By convention, the bull graph
is drawn as a triangle with the first node (0) on the bottom. The
second and third nodes (1 and 2) complete the triangle. Node 3 is
the horn connected to 1 and node 4 is the horn connected to node
2.
ALGORITHM:
Uses `NetworkX <http://networkx.lanl.gov>`_.
EXAMPLES:
Construct and show a bull graph::
sage: g = graphs.BullGraph(); g
Bull graph: Graph on 5 vertices
sage: g.show() # long time
The bull graph has 5 vertices and 5 edges. Its radius is 2, its
diameter 3, and its girth 3. The bull graph is planar with chromatic
number 3 and chromatic index also 3. ::
sage: g.order(); g.size()
5
5
sage: g.radius(); g.diameter(); g.girth()
2
3
3
sage: g.chromatic_number()
3
The bull graph has chromatic polynomial `x(x - 2)(x - 1)^3` and
Tutte polynomial `x^4 + x^3 + x^2 y`. Its characteristic polynomial
is `x(x^2 - x - 3)(x^2 + x - 1)`, which follows from the definition of
characteristic polynomials for graphs, i.e. `\det(xI - A)`, where
`x` is a variable, `A` the adjacency matrix of the graph, and `I`
the identity matrix of the same dimensions as `A`. ::
sage: chrompoly = g.chromatic_polynomial()
sage: bool(expand(x * (x - 2) * (x - 1)^3) == chrompoly)
True
sage: charpoly = g.characteristic_polynomial()
sage: M = g.adjacency_matrix(); M
[0 1 1 0 0]
[1 0 1 1 0]
[1 1 0 0 1]
[0 1 0 0 0]
[0 0 1 0 0]
sage: Id = identity_matrix(ZZ, M.nrows())
sage: D = x*Id - M
sage: bool(D.determinant() == charpoly)
True
sage: bool(expand(x * (x^2 - x - 3) * (x^2 + x - 1)) == charpoly)
True
"""
pos_dict = {0: (0, 0), 1: (-1, 1), 2: (1, 1), 3: (-2, 2), 4: (2, 2)}
import networkx
G = networkx.bull_graph()
return graph.Graph(G, pos=pos_dict, name="Bull graph")
def test_bull(self):
print '\nbull:',
self.g = nx.bull_graph()
a = programming_for_tree_decomposition(self.g, True)
self.t = a.tree_decomposition()
self.assertTrue(self.__test_all_conditions())
def test_properties_named_small_graphs(self):
G = nx.bull_graph()
assert G.number_of_nodes() == 5
assert G.number_of_edges() == 5
assert sorted(d for n, d in G.degree()) == [1, 1, 2, 3, 3]
assert nx.diameter(G) == 3
assert nx.radius(G) == 2
G = nx.chvatal_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 24
assert list(d for n, d in G.degree()) == 12 * [4]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.cubical_graph()
assert G.number_of_nodes() == 8
assert G.number_of_edges() == 12
assert list(d for n, d in G.degree()) == 8 * [3]
assert nx.diameter(G) == 3
assert nx.radius(G) == 3
G = nx.desargues_graph()
assert G.number_of_nodes() == 20
assert G.number_of_edges() == 30
assert list(d for n, d in G.degree()) == 20 * [3]
G = nx.diamond_graph()
assert G.number_of_nodes() == 4
assert sorted(d for n, d in G.degree()) == [2, 2, 3, 3]
assert nx.diameter(G) == 2
assert nx.radius(G) == 1
G = nx.dodecahedral_graph()
assert G.number_of_nodes() == 20
assert G.number_of_edges() == 30
assert list(d for n, d in G.degree()) == 20 * [3]
assert nx.diameter(G) == 5
assert nx.radius(G) == 5
G = nx.frucht_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 18
assert list(d for n, d in G.degree()) == 12 * [3]
assert nx.diameter(G) == 4
assert nx.radius(G) == 3
G = nx.heawood_graph()
assert G.number_of_nodes() == 14
assert G.number_of_edges() == 21
assert list(d for n, d in G.degree()) == 14 * [3]
assert nx.diameter(G) == 3
assert nx.radius(G) == 3
G = nx.hoffman_singleton_graph()
assert G.number_of_nodes() == 50
assert G.number_of_edges() == 175
assert list(d for n, d in G.degree()) == 50 * [7]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.house_graph()
assert G.number_of_nodes() == 5
assert G.number_of_edges() == 6
assert sorted(d for n, d in G.degree()) == [2, 2, 2, 3, 3]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.house_x_graph()
assert G.number_of_nodes() == 5
assert G.number_of_edges() == 8
assert sorted(d for n, d in G.degree()) == [2, 3, 3, 4, 4]
assert nx.diameter(G) == 2
assert nx.radius(G) == 1
G = nx.icosahedral_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 30
assert (list(
d for n, d in G.degree()) == [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5])
assert nx.diameter(G) == 3
assert nx.radius(G) == 3
G = nx.krackhardt_kite_graph()
assert G.number_of_nodes() == 10
assert G.number_of_edges() == 18
assert (sorted(
d for n, d in G.degree()) == [1, 2, 3, 3, 3, 4, 4, 5, 5, 6])
G = nx.moebius_kantor_graph()
assert G.number_of_nodes() == 16
assert G.number_of_edges() == 24
assert list(d for n, d in G.degree()) == 16 * [3]
assert nx.diameter(G) == 4
G = nx.octahedral_graph()
assert G.number_of_nodes() == 6
assert G.number_of_edges() == 12
assert list(d for n, d in G.degree()) == 6 * [4]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.pappus_graph()
assert G.number_of_nodes() == 18
assert G.number_of_edges() == 27
assert list(d for n, d in G.degree()) == 18 * [3]
assert nx.diameter(G) == 4
G = nx.petersen_graph()
assert G.number_of_nodes() == 10
assert G.number_of_edges() == 15
assert list(d for n, d in G.degree()) == 10 * [3]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.sedgewick_maze_graph()
assert G.number_of_nodes() == 8
assert G.number_of_edges() == 10
assert sorted(d for n, d in G.degree()) == [1, 2, 2, 2, 3, 3, 3, 4]
G = nx.tetrahedral_graph()
assert G.number_of_nodes() == 4
assert G.number_of_edges() == 6
assert list(d for n, d in G.degree()) == [3, 3, 3, 3]
assert nx.diameter(G) == 1
assert nx.radius(G) == 1
G = nx.truncated_cube_graph()
assert G.number_of_nodes() == 24
assert G.number_of_edges() == 36
assert list(d for n, d in G.degree()) == 24 * [3]
G = nx.truncated_tetrahedron_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 18
assert list(d for n, d in G.degree()) == 12 * [3]
G = nx.tutte_graph()
assert G.number_of_nodes() == 46
assert G.number_of_edges() == 69
assert list(d for n, d in G.degree()) == 46 * [3]
# Test create_using with directed or multigraphs on small graphs
pytest.raises(nx.NetworkXError,
nx.tutte_graph,
create_using=nx.DiGraph)
MG = nx.tutte_graph(create_using=nx.MultiGraph)
assert sorted(MG.edges()) == sorted(G.edges())
def small_graphs():
print("Make small graph")
G = nx.make_small_graph(
["adjacencylist", "C_4", 4, [[2, 4], [1, 3], [2, 4], [1, 3]]])
draw_graph(G)
G = nx.make_small_graph(
["adjacencylist", "C_4", 4, [[2, 4], [3], [4], []]])
draw_graph(G)
G = nx.make_small_graph(
["edgelist", "C_4", 4, [[1, 2], [3, 4], [2, 3], [4, 1]]])
draw_graph(G)
print("LCF graph")
G = nx.LCF_graph(6, [3, -3], 3)
draw_graph(G)
G = nx.LCF_graph(14, [5, -5], 7)
draw_graph(G)
print("Bull graph")
G = nx.bull_graph()
draw_graph(G)
print("Chvátal graph")
G = nx.chvatal_graph()
draw_graph(G)
print("Cubical graph")
G = nx.cubical_graph()
draw_graph(G)
print("Desargues graph")
G = nx.desargues_graph()
draw_graph(G)
print("Diamond graph")
G = nx.diamond_graph()
draw_graph(G)
print("Dodechaedral graph")
G = nx.dodecahedral_graph()
draw_graph(G)
print("Frucht graph")
G = nx.frucht_graph()
draw_graph(G)
print("Heawood graph")
G = nx.heawood_graph()
draw_graph(G)
print("House graph")
G = nx.house_graph()
draw_graph(G)
print("House X graph")
G = nx.house_x_graph()
draw_graph(G)
print("Icosahedral graph")
G = nx.icosahedral_graph()
draw_graph(G)
print("Krackhardt kite graph")
G = nx.krackhardt_kite_graph()
draw_graph(G)
print("Moebius kantor graph")
G = nx.moebius_kantor_graph()
draw_graph(G)
print("Octahedral graph")
G = nx.octahedral_graph()
draw_graph(G)
print("Pappus graph")
G = nx.pappus_graph()
draw_graph(G)
print("Petersen graph")
G = nx.petersen_graph()
draw_graph(G)
print("Sedgewick maze graph")
G = nx.sedgewick_maze_graph()
draw_graph(G)
print("Tetrahedral graph")
G = nx.tetrahedral_graph()
draw_graph(G)
print("Truncated cube graph")
G = nx.truncated_cube_graph()
draw_graph(G)
print("Truncated tetrahedron graph")
G = nx.truncated_tetrahedron_graph()
draw_graph(G)
print("Tutte graph")
G = nx.tutte_graph()
draw_graph(G)
def BullGraph():
r"""
Returns a bull graph with 5 nodes.
A bull graph is named for its shape. It's a triangle with horns.
This constructor depends on `NetworkX <http://networkx.lanl.gov>`_
numeric labeling. For more information, see this
:wikipedia:`Wikipedia article on the bull graph <Bull_graph>`.
PLOTTING:
Upon construction, the position dictionary is filled to
override the spring-layout algorithm. By convention, the bull graph
is drawn as a triangle with the first node (0) on the bottom. The
second and third nodes (1 and 2) complete the triangle. Node 3 is
the horn connected to 1 and node 4 is the horn connected to node
2.
ALGORITHM:
Uses `NetworkX <http://networkx.lanl.gov>`_.
EXAMPLES:
Construct and show a bull graph::
sage: g = graphs.BullGraph(); g
Bull graph: Graph on 5 vertices
sage: g.show() # long time
The bull graph has 5 vertices and 5 edges. Its radius is 2, its
diameter 3, and its girth 3. The bull graph is planar with chromatic
number 3 and chromatic index also 3. ::
sage: g.order(); g.size()
5
5
sage: g.radius(); g.diameter(); g.girth()
2
3
3
sage: g.chromatic_number()
3
The bull graph has chromatic polynomial `x(x - 2)(x - 1)^3` and
Tutte polynomial `x^4 + x^3 + x^2 y`. Its characteristic polynomial
is `x(x^2 - x - 3)(x^2 + x - 1)`, which follows from the definition of
characteristic polynomials for graphs, i.e. `\det(xI - A)`, where
`x` is a variable, `A` the adjacency matrix of the graph, and `I`
the identity matrix of the same dimensions as `A`. ::
sage: chrompoly = g.chromatic_polynomial()
sage: bool(expand(x * (x - 2) * (x - 1)^3) == chrompoly)
True
sage: charpoly = g.characteristic_polynomial()
sage: M = g.adjacency_matrix(); M
[0 1 1 0 0]
[1 0 1 1 0]
[1 1 0 0 1]
[0 1 0 0 0]
[0 0 1 0 0]
sage: Id = identity_matrix(ZZ, M.nrows())
sage: D = x*Id - M
sage: bool(D.determinant() == charpoly)
True
sage: bool(expand(x * (x^2 - x - 3) * (x^2 + x - 1)) == charpoly)
True
"""
pos_dict = {0:(0,0), 1:(-1,1), 2:(1,1), 3:(-2,2), 4:(2,2)}
import networkx
G = networkx.bull_graph()
return graph.Graph(G, pos=pos_dict, name="Bull graph")
import networkx as nx
import matplotlib.pylab as plt
from plot_multigraph import plot_multigraph
graphs = [
("bull", nx.bull_graph()),
("chvatal", nx.chvatal_graph()),
("cubical", nx.cubical_graph()),
("desargues", nx.desargues_graph()),
("diamond", nx.diamond_graph()),
("dodecahedral", nx.dodecahedral_graph()),
("frucht", nx.frucht_graph()),
("heawood", nx.heawood_graph()),
("house", nx.house_graph()),
("house_x", nx.house_x_graph()),
("icosahedral", nx.icosahedral_graph()),
("krackhardt_kite", nx.krackhardt_kite_graph()),
("moebius_kantor", nx.moebius_kantor_graph()),
("octahedral", nx.octahedral_graph()),
("pappus", nx.pappus_graph()),
("petersen", nx.petersen_graph()),
("sedgewick_maze", nx.sedgewick_maze_graph()),
("tetrahedral", nx.tetrahedral_graph()),
("truncated_cube", nx.truncated_cube_graph()),
("truncated_tetrahedron", nx.truncated_tetrahedron_graph()),
]
plot_multigraph(graphs, 4, 5, node_size=50)
plt.savefig('graphs/small.png')
mcl.prune(dfs_height)
return mcl
def clean_attributes(g):
for v in g.nodes():
if 'visited' in g.node[v]:
del g.node[v]['visited']
for u, v in g.edges():
if 'visited' in g[u][v]:
del g[u][v]['visited']
if __name__ == '__main__':
targets = {
'bull': nx.bull_graph(), # 1-connected planar
'chvatal': nx.chvatal_graph(), # 4-connected non-planar
'cubical': nx.cubical_graph(), # 3-connected planar
'desargues': nx.desargues_graph(), # 3-connected non-planar
'diamond': nx.diamond_graph(), # 2-connected planar
'dodecahedral': nx.dodecahedral_graph(), # 3-connected planar
'frucht': nx.frucht_graph(), # 3-connected planar
'heawood': nx.heawood_graph(), # 3-connected non-planar
'house': nx.house_graph(), # 2-connected planar
'house_x': nx.house_x_graph(), # 2-connected planar
'icosahedral': nx.icosahedral_graph(), # 5-connected planar
'krackhardt': nx.krackhardt_kite_graph(), # 1-connected planar
'moebius': nx.moebius_kantor_graph(), # non-planar
'octahedral': nx.octahedral_graph(), # 4-connected planar
'pappus': nx.pappus_graph(), # 3-connected non-planar
'petersen': nx.petersen_graph(), # 3-connected non-planar
import networkx as nx
import matplotlib.pylab as plt
from plot_multigraph import plot_multigraph
graphs = [
("bull", nx.bull_graph()),
("chvatal", nx.chvatal_graph()),
("cubical", nx.cubical_graph()),
("desargues", nx.desargues_graph()),
("diamond", nx.diamond_graph()),
("dodecahedral", nx.dodecahedral_graph()),
("frucht", nx.frucht_graph()),
("heawood", nx.heawood_graph()),
("house", nx.house_graph()),
("house_x", nx.house_x_graph()),
("icosahedral", nx.icosahedral_graph()),
("krackhardt_kite", nx.krackhardt_kite_graph()),
("moebius_kantor", nx.moebius_kantor_graph()),
("octahedral", nx.octahedral_graph()),
("pappus", nx.pappus_graph()),
("petersen", nx.petersen_graph()),
("sedgewick_maze", nx.sedgewick_maze_graph()),
("tetrahedral", nx.tetrahedral_graph()),
("truncated_cube", nx.truncated_cube_graph()),
("truncated_tetrahedron", nx.truncated_tetrahedron_graph()),
]
plot_multigraph(graphs, 4, 5, node_size=50)
plt.savefig('graphs/small.png')
G1 = nx.erdos_renyi_graph(n=24, p=0.3, seed=seed)
# some cool graphs
G2 = nx.star_graph(20)
G3 = nx.path_graph(30)
G4 = nx.petersen_graph()
G5 = nx.dodecahedral_graph()
G6 = nx.house_graph()
G7 = nx.moebius_kantor_graph()
G8 = nx.barabasi_albert_graph(5, 4)
G9 = nx.heawood_graph()
G10 = nx.icosahedral_graph()
G11 = nx.sedgewick_maze_graph()
G12 = nx.havel_hakimi_graph([1, 1])
G13 = nx.complete_graph(20)
G14 = nx.bull_graph()
G = G1 # choose a graph from the list
gd.draw_custom(G)
plt.show()
#exact cut
print("Time 'local_consistent_max_cut':" + str(gt.execution_time(mc.local_consistent_max_cut, 1, G)))
print('Edges cut: ' + str(gc.cut_edges(G)))
print('\n')
print("Time 'lazy_local_consistent_max_cut':" + str(gt.execution_time(mc.lazy_local_consistent_max_cut, 1, G)))
print('Edges cut: ' + str(gc.cut_edges(G)))
print('\n')
gd.draw_cut_graph(G)
pos = nx.spectral_layout(G)
break
elif mode == 14:
G = nx.truncated_cube_graph()
pos = nx.spectral_layout(G)
break
elif mode == 15:
G = nx.sedgewick_maze_graph()
pos = nx.spectral_layout(G)
break
elif mode == 16:
G = nx.pappus_graph()
pos = nx.spectral_layout(G)
break
elif mode == 17:
G = nx.bull_graph()
pos = nx.spectral_layout(G)
break
elif mode == 18:
G = nx.krackhardt_kite_graph()
break
else:
print("Please enter a valid number.")
costsChecker = int(input("Cost Mode (0 - random / 1 - cost of 1): "))
# assigns random weights to all of the edges
for (u, v) in G.edges():
if costsChecker == 0:
G.edge[u][v]['weight'] = random.randint(0, 500)
else:
def test_bull(self):
expected = True
actual = is_planar(nx.bull_graph())
self.assertEqual(expected, actual)
G1 = nx.erdos_renyi_graph(n=24, p=0.3, seed=seed)
# some cool graphs
G2 = nx.star_graph(20)
G3 = nx.path_graph(30)
G4 = nx.petersen_graph()
G5 = nx.dodecahedral_graph()
G6 = nx.house_graph()
G7 = nx.moebius_kantor_graph()
G8 = nx.barabasi_albert_graph(5, 4)
G9 = nx.heawood_graph()
G10 = nx.icosahedral_graph()
G11 = nx.sedgewick_maze_graph()
G12 = nx.havel_hakimi_graph([1, 1])
G13 = nx.complete_graph(20)
G14 = nx.bull_graph()
G = G1 # choose a graph from the list
gd.draw_custom(G)
plt.show()
#exact cut
print("Time 'local_consistent_max_cut':" +
str(gt.execution_time(mc.local_consistent_max_cut, 1, G)))
print('Edges cut: ' + str(gc.cut_edges(G)))
print('\n')
print("Time 'lazy_local_consistent_max_cut':" +
str(gt.execution_time(mc.lazy_local_consistent_max_cut, 1, G)))
print('Edges cut: ' + str(gc.cut_edges(G)))
print('\n')