def test_shell_directed():
# seeds = [3134027055, 4079264063, 1350769518, 1405643020, 530038094]
seeds = [31]
for seed in seeds:
constructor = [(12, 70, 0.8), (15, 40, 0.6)]
G = nx.random_shell_graph(constructor, seed=seed).to_directed()
_check_edge_connectivity(G)
def test_shell_directed():
# seeds = [3134027055, 4079264063, 1350769518, 1405643020, 530038094]
seeds = [31]
for seed in seeds:
constructor = [(12, 70, 0.8), (15, 40, 0.6)]
G = nx.random_shell_graph(constructor, seed=seed).to_directed()
_check_edge_connectivity(G)
class GraphType:
BALANCED_TREE = ('Balanced tree', _balanced_tree)
BARBELL = ('Barbell', lambda n: nx.barbell_graph(int(n*.4), int(n*.3)))
CIRCULAR_LADDER = ('Circular ladder', lambda n: nx.circular_ladder_graph(int(n/2)))
COMPLETE = ('Complete', lambda n: nx.complete_graph(int(n)))
COMPLETE_BIPARTITE = ('Complete bipartite',
lambda n: nx.complete_bipartite_graph(int(n*.6), int(n*.4)))
CYCLE = ('Cycle', lambda n: nx.cycle_graph(int(n)))
GRID = ('Grid', lambda n: nx.grid_graph([int(np.sqrt(n))]*2))
HYPERCUBE = ('Hypercube', _hypercube)
LADDER = ('Ladder', lambda n: nx.ladder_graph(int(n/2)))
LOBSTER = ('Lobster', lambda n: nx.random_lobster(int(n / (1 + .7 + .7*.5)), .7, .5))
LOLLIPOP = ('Lollipop', lambda n: nx.lollipop_graph(int(n/2), int(n/2)))
PATH = ('Path', lambda n: nx.path_graph(int(n)))
REGULAR = ('Regular', lambda n: nx.random_regular_graph(np.random.randint(10)*2, n))
SCALEFREE = ('Scale-free', lambda n: nx.scale_free_graph(int(n)))
SHELL = ('Shell', lambda n: nx.random_shell_graph([(int(n*.1), int(n*.1), .2),
(int(n*.3), int(n*.3), .8),
(int(n*.6), int(n*.6), .5)]))
STAR = ('Star', lambda n: nx.star_graph(int(n - 1)))
WAXMAN = ('Waxman', lambda n: nx.waxman_graph(int(n)))
WHEEL = ('Wheel', lambda n: nx.wheel_graph(int(n)))
all = (BALANCED_TREE, BARBELL, CIRCULAR_LADDER, COMPLETE, COMPLETE_BIPARTITE,
CYCLE, GRID, HYPERCUBE, LADDER, LOBSTER, LOLLIPOP, PATH, REGULAR,
SCALEFREE, SHELL, STAR, WAXMAN, WHEEL)
def test_shell():
# seeds = [2057382236, 3331169846, 1840105863, 476020778, 2247498425]
seeds = [20]
for seed in seeds:
constructor = [(12, 70, 0.8), (15, 40, 0.6)]
G = nx.random_shell_graph(constructor, seed=seed)
_check_edge_connectivity(G)
def RandomShell(constructor, seed=None):
"""
Returns a random shell graph for the constructor given.
INPUT:
- ``constructor`` - a list of 3-tuples (n,m,d), each
representing a shell
- ``n`` - the number of vertices in the shell
- ``m`` - the number of edges in the shell
- ``d`` - the ratio of inter (next) shell edges to
intra shell edges
- ``seed`` - for the random number generator
EXAMPLE::
sage: G = graphs.RandomShell([(10,20,0.8),(20,40,0.8)])
sage: G.edges(labels=False)
[(0, 3), (0, 7), (0, 8), (1, 2), (1, 5), (1, 8), (1, 9), (3, 6), (3, 11), (4, 6), (4, 7), (4, 8), (4, 21), (5, 8), (5, 9), (6, 9), (6, 10), (7, 8), (7, 9), (8, 18), (10, 11), (10, 13), (10, 19), (10, 22), (10, 26), (11, 18), (11, 26), (11, 28), (12, 13), (12, 14), (12, 28), (12, 29), (13, 16), (13, 21), (13, 29), (14, 18), (16, 20), (17, 18), (17, 26), (17, 28), (18, 19), (18, 22), (18, 27), (18, 28), (19, 23), (19, 25), (19, 28), (20, 22), (24, 26), (24, 27), (25, 27), (25, 29)]
sage: G.show() # long time
"""
if seed is None:
seed = current_randstate().long_seed()
import networkx
return Graph(networkx.random_shell_graph(constructor, seed=seed))
def RandomShell(constructor, seed=None):
"""
Returns a random shell graph for the constructor given.
INPUT:
- ``constructor`` - a list of 3-tuples (n,m,d), each
representing a shell
- ``n`` - the number of vertices in the shell
- ``m`` - the number of edges in the shell
- ``d`` - the ratio of inter (next) shell edges to
intra shell edges
- ``seed`` - for the random number generator
EXAMPLE::
sage: G = graphs.RandomShell([(10,20,0.8),(20,40,0.8)])
sage: G.edges(labels=False)
[(0, 3), (0, 7), (0, 8), (1, 2), (1, 5), (1, 8), (1, 9), (3, 6), (3, 11), (4, 6), (4, 7), (4, 8), (4, 21), (5, 8), (5, 9), (6, 9), (6, 10), (7, 8), (7, 9), (8, 18), (10, 11), (10, 13), (10, 19), (10, 22), (10, 26), (11, 18), (11, 26), (11, 28), (12, 13), (12, 14), (12, 28), (12, 29), (13, 16), (13, 21), (13, 29), (14, 18), (16, 20), (17, 18), (17, 26), (17, 28), (18, 19), (18, 22), (18, 27), (18, 28), (19, 23), (19, 25), (19, 28), (20, 22), (24, 26), (24, 27), (25, 27), (25, 29)]
sage: G.show() # long time
"""
if seed is None:
seed = current_randstate().long_seed()
import networkx
return graph.Graph(networkx.random_shell_graph(constructor, seed=seed))
def test_shell():
# seeds = [2057382236, 3331169846, 1840105863, 476020778, 2247498425]
seeds = [20]
for seed in seeds:
constructor = [(12, 70, 0.8), (15, 40, 0.6)]
G = nx.random_shell_graph(constructor, seed=seed)
_check_edge_connectivity(G)
def random_graphs():
print("Random graphs")
print("fast GNP random graph")
G = nx.fast_gnp_random_graph(n=9, p=0.4)
draw_graph(G)
print("GNP random graph")
G = nx.gnp_random_graph(n=9, p=0.1)
draw_graph(G)
print("Dense GNM random graph")
G = nx.dense_gnm_random_graph(n=19, m=28)
draw_graph(G)
print("GNM random graph")
G = nx.gnm_random_graph(n=11, m=14)
draw_graph(G)
print("Erdős Rényi graph")
G = nx.erdos_renyi_graph(n=11, p=0.4)
draw_graph(G)
print("Binomial graph")
G = nx.binomial_graph(n=45, p=0.4)
draw_graph(G)
print("Newman Watts Strogatz")
G = nx.newman_watts_strogatz_graph(n=9, k=5, p=0.4)
draw_graph(G)
print("Watts Strogatz")
G = nx.watts_strogatz_graph(n=9, k=2, p=0.4)
draw_graph(G)
print("Watts Strogatz")
G = nx.watts_strogatz_graph(n=9, k=2, p=0.4)
draw_graph(G)
print("Connected Watts Strogatz")
G = nx.connected_watts_strogatz_graph(n=8, k=2, p=0.1)
draw_graph(G)
print("Random Regular Graph")
G = nx.random_regular_graph(d=2, n=9)
draw_graph(G)
print("Barabasi Albert Graph")
G = nx.barabasi_albert_graph(n=10, m=2)
draw_graph(G)
print("Powerlow Cluster Graph")
G = nx.powerlaw_cluster_graph(n=10, m=2, p=0.2)
draw_graph(G)
print("Duplication Divergence Graph")
G = nx.duplication_divergence_graph(n=10, p=0.2)
draw_graph(G)
print("Random lobster Graph")
G = nx.random_lobster(n=10, p1=0.2, p2=0.8)
draw_graph(G)
print("Random shell Graph")
constructor = [(10, 20, 0.8), (20, 40, 0.8)]
G = nx.random_shell_graph(constructor)
draw_graph(G)
print("Random Powerlow Tree")
G = nx.random_powerlaw_tree(n=24, gamma=3)
draw_graph(G)
print("Random Powerlow Tree Sequence")
G = nx.random_powerlaw_tree(n=13, gamma=3)
draw_graph(G)
def test_shell():
constructor = [(20, 80, 0.8), (80, 180, 0.6)]
G = nx.random_shell_graph(constructor)
_check_separating_sets(G)
def test_shell():
constructor=[(20, 80, 0.8), (80, 180, 0.6)]
G = nx.random_shell_graph(constructor)
_check_connectivity(G)
def test_shell():
constructor = [(20, 80, 0.8), (80, 180, 0.6)]
G = nx.random_shell_graph(constructor)
result = nx.k_components(G)
_check_connectivity(G, result)
def test_shell():
constructor = [(20, 80, 0.8), (80, 180, 0.6)]
G = nx.random_shell_graph(constructor)
_check_connectivity(G)
def test114_random_shell_graph(self):
""" Random shell graph. """
g = nx.random_shell_graph([(7, 15, 0.4), (7, 15, 0.4)])
mate1 = mv.max_cardinality_matching(g)
mate2 = nx.max_weight_matching(g, True)
self.assertEqual(len(mate1), len(mate2))
def gen_laplacian(data_num=DATA_NUM, opt=27, cache=False):
label = None
if cache:
print('Loading cached graph')
graph = pk.load(open('tmp/g.pk', 'rb'))
else:
print('Generating graph opt {}'.format(opt))
if 1 == opt:
graph = gen_rand_graph(data_num=data_num)
if 2 == opt:
top_num = random.randint(1, data_num)
bottom_num = data_num - top_num
graph = nx.bipartite.random_graph(top_num, bottom_num, 0.9)
label = [d['bipartite'] for n, d in graph.nodes(data=True)]
elif 3 == opt:
graph = nx.balanced_tree(4, 5)
elif 4 == opt:
graph = nx.complete_graph(data_num)
elif 5 == opt:
no1 = random.randint(1, data_num)
no2 = random.randint(1, int(data_num / no1))
no3 = data_num / no1 / no2
graph = nx.complete_multipartite_graph(no1, no2, no3)
elif 6 == opt:
graph = nx.circular_ladder_graph(data_num)
elif 7 == opt:
graph = nx.cycle_graph(data_num)
elif 8 == opt:
graph = nx.dorogovtsev_goltsev_mendes_graph(5)
elif 9 == opt:
top_num = int(random.random() * data_num)
bottom_num = data_num / top_num
graph = nx.grid_2d_graph(top_num, bottom_num)
elif 10 == opt:
no1 = random.randint(1, data_num)
no2 = random.randint(1, int(data_num / no1))
no3 = data_num / no1 / no2
graph = nx.grid_graph([no1, no2, no3])
elif 11 == opt:
graph = nx.hypercube_graph(10)
elif 12 == opt:
graph = nx.ladder_graph(data_num)
elif 13 == opt:
top_num = int(random.random() * data_num)
bottom_num = data_num - top_num
graph = nx.lollipop_graph(top_num, bottom_num)
elif 14 == opt:
graph = nx.path_graph(data_num)
elif 15 == opt:
graph = nx.star_graph(data_num)
elif 16 == opt:
graph = nx.wheel_graph(data_num)
elif 17 == opt:
graph = nx.margulis_gabber_galil_graph(35)
elif 18 == opt:
graph = nx.chordal_cycle_graph(data_num)
elif 19 == opt:
graph = nx.fast_gnp_random_graph(data_num, random.random())
elif 20 == opt: # jump eigen value
graph = nx.gnp_random_graph(data_num, random.random())
elif 21 == opt: # disconnected graph
graph = nx.dense_gnm_random_graph(data_num, data_num / 2)
elif 22 == opt: # disconnected graph
graph = nx.gnm_random_graph(data_num, data_num / 2)
elif 23 == opt:
graph = nx.erdos_renyi_graph(data_num, data_num / 2)
elif 24 == opt:
graph = nx.binomial_graph(data_num, data_num / 2)
elif 25 == opt:
graph = nx.newman_watts_strogatz_graph(data_num, 5,
random.random())
elif 26 == opt:
graph = nx.watts_strogatz_graph(data_num, 5, random.random())
elif 26 == opt: # smooth eigen
graph = nx.connected_watts_strogatz_graph(data_num, 5,
random.random())
elif 27 == opt: # smooth eigen
graph = nx.random_regular_graph(5, data_num)
elif 28 == opt: # smooth eigen
graph = nx.barabasi_albert_graph(data_num, 5)
elif 29 == opt: # smooth eigen
graph = nx.powerlaw_cluster_graph(data_num, 5, random.random())
elif 30 == opt: # smooth eigen
graph = nx.duplication_divergence_graph(data_num, random.random())
elif 31 == opt:
p = random.random()
q = random.random()
graph = nx.random_lobster(data_num, p, q)
elif 32 == opt:
p = random.random()
q = random.random()
k = random.random()
graph = nx.random_shell_graph([(data_num / 3, 50, p),
(data_num / 3, 40, q),
(data_num / 3, 30, k)])
elif 33 == opt: # smooth eigen
top_num = int(random.random() * data_num)
bottom_num = data_num - top_num
graph = nx.k_random_intersection_graph(top_num, bottom_num, 3)
elif 34 == opt:
graph = nx.random_geometric_graph(data_num, .1)
elif 35 == opt:
graph = nx.waxman_graph(data_num)
elif 36 == opt:
graph = nx.geographical_threshold_graph(data_num, .5)
elif 37 == opt:
top_num = int(random.random() * data_num)
bottom_num = data_num - top_num
graph = nx.uniform_random_intersection_graph(
top_num, bottom_num, .5)
elif 39 == opt:
graph = nx.navigable_small_world_graph(data_num)
elif 40 == opt:
graph = nx.random_powerlaw_tree(data_num, tries=200)
elif 41 == opt:
graph = nx.karate_club_graph()
elif 42 == opt:
graph = nx.davis_southern_women_graph()
elif 43 == opt:
graph = nx.florentine_families_graph()
elif 44 == opt:
graph = nx.complete_multipartite_graph(data_num, data_num,
data_num)
# OPT 1
# norm_lap = nx.normalized_laplacian_matrix(graph).toarray()
# OPT 2: renormalized
# pk.dump(graph, open('tmp/g.pk', 'wb'))
# plot_graph(graph, label)
# note difference: normalized laplacian and normalzation by eigenvalue
norm_lap, eigval, eigvec = normalize_lap(graph)
return graph, norm_lap, eigval, eigvec
print
if __name__ == '__main__':
import time
try:
import igraph
except ImportError:
# it would be nice to implement biconnected_components in NetworkX
# it is in my TODO list
raise Exception('Unable to import igraph (used only to test accuracy)')
Gnp = networkx.gnp_random_graph(100, 0.03)
Gba = networkx.barabasi_albert_graph(100, 2)
Gpc = networkx.powerlaw_cluster_graph(100, 2, 0.1)
constructor = [(20, 40, 0.8), (80, 140, 0.6)]
Gshell = networkx.random_shell_graph(constructor)
Gshell.name = 'Shell graph'
deg_seq = networkx.create_degree_sequence(100,
networkx.utils.powerlaw_sequence)
Gconf = networkx.Graph(networkx.configuration_model(deg_seq))
Gconf.remove_edges_from(Gconf.selfloop_edges())
Gconf.name = 'Conf model'
Gdeb_2m = networkx.read_adjlist('test_2m.adj') # file in ticket #589
Gdeb_2m.name = "Debian 2-mode"
Gdeb_1m = networkx.read_adjlist('test_1m.adj') # file in ticket #589
Gdeb_1m.name = "Debian 1-mode"
graph_list = [Gnp, Gba, Gpc, Gshell, Gconf, Gdeb_1m, Gdeb_2m]
for G in graph_list:
print("Testing with %s" % G.name)
print(networkx.info(G))
print('Running analysis ...')
def test_shell():
constructor = [(20, 80, 0.8), (80, 180, 0.6)]
G = nx.random_shell_graph(constructor)
result = nx.k_components(G)
_check_connectivity(G, result)
len([v for v in result[2][0] if v not in bnodes])))
print
if __name__ == '__main__':
import time
try:
import igraph
except ImportError:
# it would be nice to implement biconnected_components in NetworkX
# it is in my TODO list
raise Exception('Unable to import igraph (used only to test accuracy)')
Gnp = networkx.gnp_random_graph(100, 0.03)
Gba = networkx.barabasi_albert_graph(100, 2)
Gpc = networkx.powerlaw_cluster_graph(100, 2, 0.1)
constructor=[(20,40,0.8),(80,140,0.6)]
Gshell = networkx.random_shell_graph(constructor)
Gshell.name = 'Shell graph'
deg_seq = networkx.create_degree_sequence(100, networkx.utils.powerlaw_sequence)
Gconf = networkx.Graph(networkx.configuration_model(deg_seq))
Gconf.remove_edges_from(Gconf.selfloop_edges())
Gconf.name = 'Conf model'
Gdeb_2m = networkx.read_adjlist('test_2m.adj') # file in ticket #589
Gdeb_2m.name = "Debian 2-mode"
Gdeb_1m = networkx.read_adjlist('test_1m.adj') # file in ticket #589
Gdeb_1m.name = "Debian 1-mode"
graph_list = [Gnp, Gba, Gpc, Gshell, Gconf, Gdeb_1m, Gdeb_2m]
for G in graph_list:
print("Testing with %s"%G.name)
print(networkx.info(G))
print('Running analysis ...')
print
def test_random_graph(self):
seed = 42
G = nx.gnp_random_graph(100, 0.25, seed)
G = nx.gnp_random_graph(100, 0.25, seed, directed=True)
G = nx.binomial_graph(100, 0.25, seed)
G = nx.erdos_renyi_graph(100, 0.25, seed)
G = nx.fast_gnp_random_graph(100, 0.25, seed)
G = nx.fast_gnp_random_graph(100, 0.25, seed, directed=True)
G = nx.gnm_random_graph(100, 20, seed)
G = nx.gnm_random_graph(100, 20, seed, directed=True)
G = nx.dense_gnm_random_graph(100, 20, seed)
G = nx.watts_strogatz_graph(10, 2, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = nx.connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed)
assert len(G) == 10
assert G.number_of_edges() == 10
pytest.raises(nx.NetworkXError,
nx.connected_watts_strogatz_graph,
10,
2,
0.1,
tries=0)
G = nx.watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 20
G = nx.newman_watts_strogatz_graph(10, 2, 0.0, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = nx.newman_watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() >= 20
G = nx.barabasi_albert_graph(100, 1, seed)
G = nx.barabasi_albert_graph(100, 3, seed)
assert G.number_of_edges() == (97 * 3)
G = nx.barabasi_albert_graph(100, 3, seed, nx.complete_graph(5))
assert G.number_of_edges() == (10 + 95 * 3)
G = nx.extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert G.number_of_edges() == 99
G = nx.extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert G.number_of_edges() == 97 * 3
G = nx.extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert G.number_of_edges() == 99
G = nx.extended_barabasi_albert_graph(100, 2, 0.5, 0, seed)
assert G.number_of_edges() > 100 * 3
assert G.number_of_edges() < 100 * 4
G = nx.extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed)
assert G.number_of_edges() > 100 * 2
assert G.number_of_edges() < 100 * 4
G = nx.powerlaw_cluster_graph(100, 1, 1.0, seed)
G = nx.powerlaw_cluster_graph(100, 3, 0.0, seed)
assert G.number_of_edges() == (97 * 3)
G = nx.random_regular_graph(10, 20, seed)
pytest.raises(nx.NetworkXError, nx.random_regular_graph, 3, 21)
pytest.raises(nx.NetworkXError, nx.random_regular_graph, 33, 21)
constructor = [(10, 20, 0.8), (20, 40, 0.8)]
G = nx.random_shell_graph(constructor, seed)
def is_caterpillar(g):
"""
A tree is a caterpillar iff all nodes of degree >=3 are surrounded
by at most two nodes of degree two or greater.
ref: http://mathworld.wolfram.com/CaterpillarGraph.html
"""
deg_over_3 = [n for n in g if g.degree(n) >= 3]
for n in deg_over_3:
nbh_deg_over_2 = [
nbh for nbh in g.neighbors(n) if g.degree(nbh) >= 2
]
if not len(nbh_deg_over_2) <= 2:
return False
return True
def is_lobster(g):
"""
A tree is a lobster if it has the property that the removal of leaf
nodes leaves a caterpillar graph (Gallian 2007)
ref: http://mathworld.wolfram.com/LobsterGraph.html
"""
non_leafs = [n for n in g if g.degree(n) > 1]
return is_caterpillar(g.subgraph(non_leafs))
G = nx.random_lobster(10, 0.1, 0.5, seed)
assert max([G.degree(n) for n in G.nodes()]) > 3
assert is_lobster(G)
pytest.raises(nx.NetworkXError, nx.random_lobster, 10, 0.1, 1, seed)
pytest.raises(nx.NetworkXError, nx.random_lobster, 10, 1, 1, seed)
pytest.raises(nx.NetworkXError, nx.random_lobster, 10, 1, 0.5, seed)
# docstring says this should be a caterpillar
G = nx.random_lobster(10, 0.1, 0.0, seed)
assert is_caterpillar(G)
# difficult to find seed that requires few tries
seq = nx.random_powerlaw_tree_sequence(10, 3, seed=14, tries=1)
G = nx.random_powerlaw_tree(10, 3, seed=14, tries=1)
def test_shell():
constructor = [(20, 80, 0.8), (80, 180, 0.6)]
G = nx.random_shell_graph(constructor)
_check_separating_sets(G)
def test113_random_shell_graph(self):
""" Random shell graph. """
g = nx.random_shell_graph([(50, 200, 0.5), (50, 300, 0.5)])
mate2 = nx.max_weight_matching(g, True)
mate1 = mv.max_cardinality_matching(g)
self.assertEqual(len(mate1), len(mate2))
