def test_joint_degree_graph(ntimes=10):
for _ in range(ntimes):
seed = int(time.time())
n, m, p = 20, 10, 1
# generate random graph with model powerlaw_cluster and calculate
# its joint degree
g = powerlaw_cluster_graph(n, m, p, seed=seed)
joint_degrees_g = degree_mixing_dict(g, normalized=False)
# generate simple undirected graph with given joint degree
# joint_degrees_g
G = joint_degree_graph(joint_degrees_g)
joint_degrees_G = degree_mixing_dict(G, normalized=False)
# assert that the given joint degree is equal to the generated
# graph's joint degree
assert_true(joint_degrees_g == joint_degrees_G)
def test_joint_degree_graph(ntimes=10):
for _ in range(ntimes):
seed = int(time.time())
n, m, p = 20, 10, 1
# generate random graph with model powerlaw_cluster and calculate
# its joint degree
g = powerlaw_cluster_graph(n, m, p, seed=seed)
joint_degrees_g = degree_mixing_dict(g, normalized=False)
# generate simple undirected graph with given joint degree
# joint_degrees_g
G = joint_degree_graph(joint_degrees_g)
joint_degrees_G = degree_mixing_dict(G, normalized=False)
# assert that the given joint degree is equal to the generated
# graph's joint degree
assert_true(joint_degrees_g == joint_degrees_G)
def test_directed_joint_degree_graph(n=15, m=100, ntimes=1000):
for _ in range(ntimes):
# generate gnm random graph and calculate its joint degree.
g = gnm_random_graph(n, m, None, directed=True)
# in-degree seqeunce of g as a list of integers.
in_degrees = list(dict(g.in_degree()).values())
# out-degree sequence of g as a list of integers.
out_degrees = list(dict(g.out_degree()).values())
nkk = degree_mixing_dict(g)
# generate simple directed graph with given degree sequence and joint
# degree matrix.
G = directed_joint_degree_graph(in_degrees, out_degrees, nkk)
# assert degree sequence correctness.
assert in_degrees == list(dict(G.in_degree()).values())
assert out_degrees == list(dict(G.out_degree()).values())
# assert joint degree matrix correctness.
assert nkk == degree_mixing_dict(G)
