def test_dual_barabasi_albert(self, m1=1, m2=4, p=0.5):
"""
Tests that the dual BA random graph generated behaves consistently.
Tests the exceptions are raised as expected.
The graphs generation are repeated several times to prevent lucky shots
"""
seed = 42
repeats = 2
while repeats:
repeats -= 1
# This should be BA with m = m1
BA1 = barabasi_albert_graph(100, m1, seed)
DBA1 = dual_barabasi_albert_graph(100, m1, m2, 1, seed)
assert_equal(BA1.size(), DBA1.size())
# This should be BA with m = m2
BA2 = barabasi_albert_graph(100, m2, seed)
DBA2 = dual_barabasi_albert_graph(100, m1, m2, 0, seed)
assert_equal(BA2.size(), DBA2.size())
# Testing exceptions
dbag = dual_barabasi_albert_graph
assert_raises(NetworkXError, dbag, m1, m1, m2, 0)
assert_raises(NetworkXError, dbag, m2, m1, m2, 0)
assert_raises(NetworkXError, dbag, 100, m1, m2, -0.5)
assert_raises(NetworkXError, dbag, 100, m1, m2, 1.5)
def test_dual_barabasi_albert(self, m1=1, m2=4, p=0.5):
"""
Tests that the dual BA random graph generated behaves consistently.
Tests the exceptions are raised as expected.
The graphs generation are repeated several times to prevent lucky shots
"""
seed = 42
repeats = 2
while repeats:
repeats -= 1
# This should be BA with m = m1
BA1 = barabasi_albert_graph(100, m1, seed)
DBA1 = dual_barabasi_albert_graph(100, m1, m2, 1, seed)
assert BA1.size() == DBA1.size()
# This should be BA with m = m2
BA2 = barabasi_albert_graph(100, m2, seed)
DBA2 = dual_barabasi_albert_graph(100, m1, m2, 0, seed)
assert BA2.size() == DBA2.size()
# Testing exceptions
dbag = dual_barabasi_albert_graph
pytest.raises(NetworkXError, dbag, m1, m1, m2, 0)
pytest.raises(NetworkXError, dbag, m2, m1, m2, 0)
pytest.raises(NetworkXError, dbag, 100, m1, m2, -0.5)
pytest.raises(NetworkXError, dbag, 100, m1, m2, 1.5)
def smoke_test_random_graph(self):
seed = 42
G=gnp_random_graph(100,0.25,seed)
G=binomial_graph(100,0.25,seed)
G=erdos_renyi_graph(100,0.25,seed)
G=fast_gnp_random_graph(100,0.25,seed)
G=gnm_random_graph(100,20,seed)
G=dense_gnm_random_graph(100,20,seed)
G=watts_strogatz_graph(10,2,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=connected_watts_strogatz_graph(10,2,0.1,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 20)
G=newman_watts_strogatz_graph(10,2,0.0,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=newman_watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_true(G.number_of_edges() >= 20)
G=barabasi_albert_graph(100,1,seed)
G=barabasi_albert_graph(100,3,seed)
assert_equal(G.number_of_edges(),(97*3))
G = extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert_equal(G.number_of_edges(), 99)
G = extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert_equal(G.number_of_edges(), 97 * 3)
G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert_equal(G.number_of_edges(), 99)
G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed)
assert_greater(G.number_of_edges(), 100 * 3)
assert_less(G.number_of_edges(), 100 * 4)
G=extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed)
assert_greater(G.number_of_edges(), 100 * 2)
assert_less(G.number_of_edges(), 100 * 4)
G=powerlaw_cluster_graph(100,1,1.0,seed)
G=powerlaw_cluster_graph(100,3,0.0,seed)
assert_equal(G.number_of_edges(),(97*3))
G=random_regular_graph(10,20,seed)
assert_raises(NetworkXError, random_regular_graph, 3, 21)
constructor=[(10,20,0.8),(20,40,0.8)]
G=random_shell_graph(constructor,seed)
G=random_lobster(10,0.1,0.5,seed)
def smoke_test_random_graph(self):
seed = 42
G=gnp_random_graph(100,0.25,seed)
G=binomial_graph(100,0.25,seed)
G=erdos_renyi_graph(100,0.25,seed)
G=fast_gnp_random_graph(100,0.25,seed)
G=gnm_random_graph(100,20,seed)
G=dense_gnm_random_graph(100,20,seed)
G=watts_strogatz_graph(10,2,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=connected_watts_strogatz_graph(10,2,0.1,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 20)
G=newman_watts_strogatz_graph(10,2,0.0,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=newman_watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_true(G.number_of_edges() >= 20)
G=barabasi_albert_graph(100,1,seed)
G=barabasi_albert_graph(100,3,seed)
assert_equal(G.number_of_edges(),(97*3))
G = extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert_equal(G.number_of_edges(), 99)
G = extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert_equal(G.number_of_edges(), 97 * 3)
G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert_equal(G.number_of_edges(), 99)
G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed)
assert_greater(G.number_of_edges(), 100 * 3)
assert_less(G.number_of_edges(), 100 * 4)
G=extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed)
assert_greater(G.number_of_edges(), 100 * 2)
assert_less(G.number_of_edges(), 100 * 4)
G=powerlaw_cluster_graph(100,1,1.0,seed)
G=powerlaw_cluster_graph(100,3,0.0,seed)
assert_equal(G.number_of_edges(),(97*3))
G=random_regular_graph(10,20,seed)
assert_raises(NetworkXError, random_regular_graph, 3, 21)
constructor=[(10,20,0.8),(20,40,0.8)]
G=random_shell_graph(constructor,seed)
G=random_lobster(10,0.1,0.5,seed)
def main(): # ランダムなトポロジを生成(Barabási-Albertモデルに従う)
print("Requirement: 1 <= complexity < switchNum")
switchNum = input("スイッチ数(switchNum): ")
complexity = input("新しいノードから既存のノードに接続するエッジの数(complexity): ")
# 入力値の妥当性を確認 (1 <= complexity < switchNum)
if (type(switchNum) is not int) or (type(complexity) is not int) or (not 1 <= complexity < switchNum):
print("数値以外が入力されたか、上記の制約を満たしていません")
exit()
# 指定数のスイッチとホストを生成し接続
makeSwitch(switchNum)
makeHostAndLink(switchNum)
# Barabási-Albertモデルのグラフを生成
G = barabasi_albert_graph(switchNum, complexity)
print(G.edges())
# 生成されたグラフに基づいてスイッチ間を接続
makeLinks(G.edges())
# グラフをpngで出力
networkx.draw(G, with_labels=True)
pngpath = "test/topo_image/ba_random_" + \
str(switchNum) + "_" + str(complexity) + ".png"
pyplot.savefig(pngpath)
# pyplot.show()
# ファイルに出力
path = "test/ba_random_" + str(switchNum) + "_" + str(complexity) + ".conf"
if os.path.exists(path):
os.remove(path)
with open(path, mode='w') as file:
file.write('\n'.join(conf))
print "\nDone."
def main():
# デフォルト引数処理
if sys.argv and len(sys.argv) == 3:
switchNum = int(sys.argv[1])
complexity = int(sys.argv[2])
else:
switchNum = 30
complexity = 2
# Barabási-Albertモデルのグラフを生成
tmp = barabasi_albert_graph(switchNum, complexity)
# ノード番号を1からに
edges = []
for edge in tmp.edges():
e = list(edge)
e[0] += 1
e[1] += 1
edges.append(tuple(e))
sorted(edges, key=lambda e: (e[0], e[1]))
print(edges)
# edgesから無向グラフ作成
G = networkx.Graph()
G.add_edges_from(edges)
# 外部出力
if os.path.exists('.edges'):
os.remove('.edges')
with open('.edges', 'w') as f:
for l in sorted(G.edges(), key=lambda e: (e[0], e[1])):
f.write(str(l) + "\n")
def main():
# デフォルト引数処理
if sys.argv and len(sys.argv) == 3:
switchNum = int(sys.argv[1])
complexity = int(sys.argv[2])
else:
switchNum = 30
complexity = 2
# Barabási-Albertモデルのグラフを生成
tmp = barabasi_albert_graph(switchNum, complexity)
# ノード番号を1からに
edges = []
for edge in tmp.edges():
e = list(edge)
e[0] += 1
e[1] += 1
edges.append(tuple(e))
sorted(edges, key=lambda e: (e[0], e[1]))
print(edges)
# edgesから無向グラフ作成
G = networkx.Graph()
G.add_edges_from(edges)
# 外部出力
if os.path.exists('.edges'):
os.remove('.edges')
with open('.edges', 'w') as f:
for l in sorted(G.edges(), key=lambda e: (e[0], e[1])):
f.write(str(l) + "\n")
# networkx.nx_agraph.view_pygraphviz(G, prog='dot')
networkx.draw(G, prog="dot", with_labels=True)
pngpath = ".topo_ba.png"
pyplot.savefig(pngpath)
def main(arguments):
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('model',
help="Generative model for the random graph",
type=str)
parser.add_argument('node_number', help="Number of nodes", type=int)
parser.add_argument('-p',
'--params',
help="Model specific parameter for the graph",
type=float,
nargs='*')
parser.add_argument('-s',
'--seed',
help="Seed for the RNG",
type=int,
default=42)
args = parser.parse_args(arguments)
if args.model == "gnp":
p = args.params[0]
g = gg.fast_gnp_random_graph(args.node_number, p, args.seed)
for e in g.edges():
print("{0} {1}".format(e[0], e[1]))
if args.model == "gnm":
m = args.params[0]
g = gg.gnm_random_graph(args.node_number, m, args.seed)
for e in g.edges():
print("{0} {1}".format(e[0], e[1]))
elif args.model == "ba":
m = int(args.params[0])
g = gg.barabasi_albert_graph(args.node_number, m, args.seed)
for e in g.edges():
print("{0} {1}".format(e[0], e[1]))
elif args.model == "PL": #random graph with power-law distribution
kmin = args.params[0]
exponent = args.params[1]
kmax = np.sqrt(
args.node_number) * kmin * (exponent - 1.0) / (exponent - 2.0)
degree_vector = np.arange(kmin, kmax + 1)
degree_distribution = degree_vector**(-exponent)
degree_distribution /= np.sum(degree_distribution)
degree_distribution = np.concatenate(
(np.array([0] * int(kmin)), degree_distribution))
cumulative_degree_distribution = np.array([
np.sum(degree_distribution[0:i])
for i in range(len(degree_distribution) + 1)
])
#generate n expected degree from the dist
u_vec = random(args.node_number)
expected_degree_sequence = [
np.searchsorted(cumulative_degree_distribution, u, side='right') -
1 for u in u_vec
]
output_edge_PL(expected_degree_sequence)
def generate_subgraph(self, n_nodes_in_subgraph, **kwargs):
"""
Generate a subgraph with specified properties.
Args
- n_nodes_in_subgraph (int): number of nodes in each subgraph
Return
- G (networkx object): subgraph
"""
subgraph_generator = kwargs.pop('subgraph_generator', 'path')
if subgraph_generator == 'cycle':
G = nx.cycle_graph(n_nodes_in_subgraph)
elif subgraph_generator == 'path':
G = nx.path_graph(n_nodes_in_subgraph)
elif subgraph_generator == 'house':
G = nx.house_graph()
elif subgraph_generator == 'complete':
G = nx.complete_graph(n_nodes_in_subgraph)
elif subgraph_generator == 'star':
G = nx.star_graph(n_nodes_in_subgraph)
elif subgraph_generator == 'barabasi_albert':
m = kwargs.get('m', 5)
G = barabasi_albert_graph(n_nodes_in_subgraph,
m,
seed=config.RANDOM_SEED)
elif subgraph_generator == 'extended_barabasi_albert':
m = kwargs.get('m', 5)
p = kwargs.get('p', 0.5)
q = kwargs.get('q', 0)
G = extended_barabasi_albert_graph(n_nodes_in_subgraph,
m,
p,
q,
seed=config.RANDOM_SEED)
elif subgraph_generator == 'duplication_divergence_graph':
p = kwargs.get('p', 0.5)
G = duplication_divergence_graph(n_nodes_in_subgraph, p)
else:
raise Exception(
'The subgraph generator you specified is not implemented.')
return G
def generate_base_graph(self, **kwargs):
"""
Generate the base graph.
Return
- G (networkx object): base graph
"""
if self.base_graph_type == 'barabasi_albert':
m = kwargs.get('m', 5)
n = kwargs.get('n', 500)
G = barabasi_albert_graph(n, m, seed=config.RANDOM_SEED)
elif self.base_graph_type == 'duplication_divergence_graph':
n = kwargs.get('n', 500)
p = kwargs.get('p', 0.5)
G = duplication_divergence_graph(n, p, seed=config.RANDOM_SEED)
else:
raise Exception('The base graph you specified is not implemented')
return G
def main():
# Deal with arguments
parser = argparse.ArgumentParser()
parser.add_argument(
'N',
action="store",
type=int,
help='nodes in BA and DMS'
)
parser.add_argument(
'n_samples',
action="store",
nargs='?',
default=10,
type=int,
help='number of samples to run'
)
args = parser.parse_args()
N = args.N
SAMPLES = args.n_samples
clustersBA = []
clustersDMS = []
for n in range(SAMPLES):
print(str(n) + ' sample started')
g = barabasi_albert_graph(N, 2)
print('\tba created')
cluster1 = nx_average_clustering_per_k(g)
clustersBA.append(cluster1)
print('\tba averaged')
g = create_DMS(N)
print('\tdms created')
cluster2 = nx_average_clustering_per_k(g)
clustersDMS.append(cluster2)
gc.collect()
print('\tdms averaged')
ba = np.asarray(clustersBA)
dms = np.asarray(clustersDMS)
np.savetxt('./results/clustering_coefficient/'+ str(N) + '-ba.out', ba, delimiter=',')
np.savetxt('./results/clustering_coefficient/'+ str(N) + '-dms.out', dms, delimiter=',')
def _createGraph(self):
dlg = PropertyViewer(self.name, self.icon,
Nodes=Integer(5, 1, 100, 1),
Degree=Integer(2,1,100,1))
nodes = []
edges = []
if dlg.exec_():
values = dlg.values()
n = values['Nodes']
m = values['Degree']
G = rnd.barabasi_albert_graph(n, m)
nodes = layout.circularNodes(n, 25)
for i in G.edges_iter():
edges.append(i)
return nodes, edges
def test_extended_barabasi_albert(self, m=2):
"""
Tests that the extended BA random graph generated behaves consistently.
Tests the exceptions are raised as expected.
The graphs generation are repeated several times to prevent lucky-shots
"""
seed = 42
repeats = 2
BA_model = barabasi_albert_graph(100, m, seed)
BA_model_edges = BA_model.number_of_edges()
while repeats:
repeats -= 1
# This behaves just like BA, the number of edges must be the same
G1 = extended_barabasi_albert_graph(100, m, 0, 0, seed)
assert_equal(G1.size(), BA_model_edges)
# More than twice more edges should have been added
G1 = extended_barabasi_albert_graph(100, m, 0.8, 0, seed)
assert_greater(G1.size(), BA_model_edges * 2)
# Only edge rewiring, so the number of edges less than original
G2 = extended_barabasi_albert_graph(100, m, 0, 0.8, seed)
assert_equal(G2.size(), BA_model_edges)
# Mixed scenario: less edges than G1 and more edges than G2
G3 = extended_barabasi_albert_graph(100, m, 0.3, 0.3, seed)
assert_greater(G3.size(), G2.size())
assert_less(G3.size(), G1.size())
# Testing exceptions
ebag = extended_barabasi_albert_graph
assert_raises(NetworkXError, ebag, m, m, 0, 0)
assert_raises(NetworkXError, ebag, 1, 0.5, 0, 0)
assert_raises(NetworkXError, ebag, 100, 2, 0.5, 0.5)
def test_extended_barabasi_albert(self, m=2):
"""
Tests that the extended BA random graph generated behaves consistently.
Tests the exceptions are raised as expected.
The graphs generation are repeated several times to prevent lucky-shots
"""
seed = 42
repeats = 2
BA_model = barabasi_albert_graph(100, m, seed)
BA_model_edges = BA_model.number_of_edges()
while repeats:
repeats -= 1
# This behaves just like BA, the number of edges must be the same
G1 = extended_barabasi_albert_graph(100, m, 0, 0, seed)
assert_equal(G1.size(), BA_model_edges)
# More than twice more edges should have been added
G1 = extended_barabasi_albert_graph(100, m, 0.8, 0, seed)
assert_greater(G1.size(), BA_model_edges * 2)
# Only edge rewiring, so the number of edges less than original
G2 = extended_barabasi_albert_graph(100, m, 0, 0.8, seed)
assert_equal(G2.size(), BA_model_edges)
# Mixed scenario: less edges than G1 and more edges than G2
G3 = extended_barabasi_albert_graph(100, m, 0.3, 0.3, seed)
assert_greater(G3.size(), G2.size())
assert_less(G3.size(), G1.size())
# Testing exceptions
ebag = extended_barabasi_albert_graph
assert_raises(NetworkXError, ebag, m, m, 0, 0)
assert_raises(NetworkXError, ebag, 1, 0.5, 0, 0)
assert_raises(NetworkXError, ebag, 100, 2, 0.5, 0.5)
def barabasi_albert_generator(args):
return generators.barabasi_albert_graph(args.num_nodes, args.num_edges)
def smoke_test_random_graph(self):
seed = 42
G = gnp_random_graph(100, 0.25, seed)
G = gnp_random_graph(100, 0.25, seed, directed=True)
G = binomial_graph(100, 0.25, seed)
G = erdos_renyi_graph(100, 0.25, seed)
G = fast_gnp_random_graph(100, 0.25, seed)
G = fast_gnp_random_graph(100, 0.25, seed, directed=True)
G = gnm_random_graph(100, 20, seed)
G = gnm_random_graph(100, 20, seed, directed=True)
G = dense_gnm_random_graph(100, 20, seed)
G = watts_strogatz_graph(10, 2, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed)
assert len(G) == 10
assert G.number_of_edges() == 10
pytest.raises(NetworkXError, connected_watts_strogatz_graph, \
10, 2, 0.1, tries=0)
G = watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 20
G = newman_watts_strogatz_graph(10, 2, 0.0, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = newman_watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() >= 20
G = barabasi_albert_graph(100, 1, seed)
G = barabasi_albert_graph(100, 3, seed)
assert G.number_of_edges() == (97 * 3)
G = extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert G.number_of_edges() == 99
G = extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert G.number_of_edges() == 97 * 3
G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert G.number_of_edges() == 99
G = 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 = 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 = powerlaw_cluster_graph(100, 1, 1.0, seed)
G = powerlaw_cluster_graph(100, 3, 0.0, seed)
assert G.number_of_edges() == (97 * 3)
G = random_regular_graph(10, 20, seed)
pytest.raises(NetworkXError, random_regular_graph, 3, 21)
pytest.raises(NetworkXError, random_regular_graph, 33, 21)
constructor = [(10, 20, 0.8), (20, 40, 0.8)]
G = random_shell_graph(constructor, seed)
G = random_lobster(10, 0.1, 0.5, seed)
# difficult to find seed that requires few tries
seq = random_powerlaw_tree_sequence(10, 3, seed=14, tries=1)
G = random_powerlaw_tree(10, 3, seed=14, tries=1)
def _generate_graph(self, n): return barabasi_albert_graph(n, self._params['m'])
all_degrees = g.degree() # list of (vertex, degree)
for deg in all_degrees:
deg_hist[deg[1]] += 1
# average dist
deg_dist = np.asarray(deg_hist) / g.number_of_nodes()
return deg_dist
N = 100000
print('Networks of size ' + str(N) + '\n')
SAMPLES = 10
distsBA = []
distsDMS = []
for n in range(SAMPLES):
print(str(n) + ' sample started')
g = barabasi_albert_graph(N, 2)
print('\tba created')
dist1 = degree_distribution(g)
distsBA.append(dist1)
print('\tba averaged')
g = create_DMS(N)
print('\tdms created')
dist2 = degree_distribution(g)
distsDMS.append(dist2)
print('\tdms averaged')
gc.collect()
ba = np.asarray(distsBA)
dms = np.asarray(distsDMS)
degreesBA = []
else:
max_index = int(max_index)
max_value = Q[action, max_index]
Q[current_state, action] = R[current_state, action] + gamma * max_value
print('max_value', R[current_state, action] + gamma * max_value)
if (np.max(Q) > 0):
return (np.sum(Q / np.max(Q) * 100))
else:
return (0)
# First we generate a random graph holding a power law link distribution. This
# has the aim of keeping entropy sourced phenomena in mind.
Graph = barabasi_albert_graph(MATRIX_SIZE, int(MATRIX_SIZE / LINK_FRACTION))
pos = nx.spring_layout(Graph)
nx.draw_networkx_nodes(Graph, pos)
nx.draw_networkx_edges(Graph, pos)
nx.draw_networkx_labels(Graph, pos)
plt.show()
R = nx.adjacency_matrix(Graph).todense() - 1
Q = np.matrix(np.zeros([MATRIX_SIZE, MATRIX_SIZE]))
points_list = [t for t in zip(*np.where(R == 0))]
for point in points_list:
print(point)
if point[1] == goal:
R[point] = 100
else:
def generate_graph(n,p): g = random_graphs.barabasi_albert_graph(n, p, seed=None) a = nx.adjacency_matrix(g) return g, a
def generate_random_graph(n, m):
print "Building random graph"
G = barabasi_albert_graph(n, m, 10)
return G
import graph_tool
import graph_tool.all as gt
from networkx.generators.random_graphs import barabasi_albert_graph
from networkx.algorithms.shortest_paths.generic import average_shortest_path_length
from networkx.readwrite.gml import write_gml
import numpy as np
import time
if __name__ == '__main__':
g = barabasi_albert_graph(int(1e4), 1)
start_t = time.process_time()
average_shortest_path_length(g)
print(time.process_time() - start_t)
write_gml(g, './graph.gml')
g = gt.load_graph('./graph.gml')
start_t = time.process_time()
all_sp = gt.shortest_distance(g)
vertex_avgs = graph_tool.stats.vertex_average(g, all_sp)
avg_path = np.sum(vertex_avgs[0]) / (g.num_vertices() - 1)
print(time.process_time() - start_t)
start_t = time.process_time()
sum([sum(i) for i in gt.shortest_distance(g)
]) / (g.num_vertices()**2 - g.num_vertices())
print(time.process_time() - start_t)
return gt.local_clustering(g).get_array()
def max_degree(g):
return gt_stats.vertex_hist(g, 'total')[1][-2]
def page_rank(g):
# return gt_stats.vertex_hist(g, gt.pagerank(g))
return gt.pagerank(g).get_array()
def variance(g):
degree_hist = gt_stats.vertex_hist(g, 'total')[0] / g.num_vertices()
second_m = np.sum(degree_hist * (np.arange(len(degree_hist)) ** 2))
return second_m - avg_degree(g) ** 2
if __name__ == '__main__':
nx_g = barabasi_albert_graph(int(1e3), 2)
nx_apl = average_shortest_path_length(nx_g)
nx_ad = 2 * nx_g.number_of_edges() / nx_g.number_of_nodes()
nx_gcc = transitivity(nx_g)
nx_lcc = average_clustering(nx_g)
nx_md = len(degree_histogram(nx_g)) - 1
nx_drogc = max(connected_component_subgraphs(nx_g), key=len).number_of_nodes() / nx_g.number_of_nodes()
second_m = np.sum(np.array(degree_histogram(nx_g)) * (np.arange(len(degree_histogram(nx_g))) ** 2))
nx_v = math.sqrt(second_m - nx_ad ** 2)
nx_ap = degree_pearson_correlation_coefficient(nx_g)
nx_aknn = np.flip(np.array(
[it[1] for it in sorted(
average_degree_connectivity(nx_g).items(), reverse=True
)]
))
def test_random_graph(self):
seed = 42
G = gnp_random_graph(100, 0.25, seed)
G = gnp_random_graph(100, 0.25, seed, directed=True)
G = binomial_graph(100, 0.25, seed)
G = erdos_renyi_graph(100, 0.25, seed)
G = fast_gnp_random_graph(100, 0.25, seed)
G = fast_gnp_random_graph(100, 0.25, seed, directed=True)
G = gnm_random_graph(100, 20, seed)
G = gnm_random_graph(100, 20, seed, directed=True)
G = dense_gnm_random_graph(100, 20, seed)
G = watts_strogatz_graph(10, 2, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed)
assert len(G) == 10
assert G.number_of_edges() == 10
pytest.raises(NetworkXError,
connected_watts_strogatz_graph,
10,
2,
0.1,
tries=0)
G = watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 20
G = newman_watts_strogatz_graph(10, 2, 0.0, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = newman_watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() >= 20
G = barabasi_albert_graph(100, 1, seed)
G = barabasi_albert_graph(100, 3, seed)
assert G.number_of_edges() == (97 * 3)
G = extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert G.number_of_edges() == 99
G = extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert G.number_of_edges() == 97 * 3
G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert G.number_of_edges() == 99
G = 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 = 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 = powerlaw_cluster_graph(100, 1, 1.0, seed)
G = powerlaw_cluster_graph(100, 3, 0.0, seed)
assert G.number_of_edges() == (97 * 3)
G = random_regular_graph(10, 20, seed)
pytest.raises(NetworkXError, random_regular_graph, 3, 21)
pytest.raises(NetworkXError, random_regular_graph, 33, 21)
constructor = [(10, 20, 0.8), (20, 40, 0.8)]
G = 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 = 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(NetworkXError, random_lobster, 10, 0.1, 1, seed)
pytest.raises(NetworkXError, random_lobster, 10, 1, 1, seed)
pytest.raises(NetworkXError, random_lobster, 10, 1, 0.5, seed)
# docstring says this should be a caterpillar
G = random_lobster(10, 0.1, 0.0, seed)
assert is_caterpillar(G)
# difficult to find seed that requires few tries
seq = random_powerlaw_tree_sequence(10, 3, seed=14, tries=1)
G = random_powerlaw_tree(10, 3, seed=14, tries=1)
from networkx.generators.random_graphs import barabasi_albert_graph
from networkx.generators.directed import scale_free_graph
if __name__ == "__main__":
g = barabasi_albert_graph(300000, 1)
#g = scale_free_graph(258000)
with open("scale_free.txt", "w") as random_output:
for v, neibdict in g.adjacency():
first = True
for w, attr in neibdict.items():
if first:
random_output.write(str(w))
first = False
else:
random_output.write(" " + str(w))
random_output.write("\n")