def test_independent_cascade(self):
G = networkx.DiGraph()
G.add_edge(1, 2, act_prob=.5)
G.add_edge(2, 1, act_prob=.5)
G.add_edge(1, 3, act_prob=.2)
G.add_edge(3, 1, act_prob=.2)
G.add_edge(2, 3, act_prob=.3)
G.add_edge(2, 4, act_prob=.5)
G.add_edge(3, 4, act_prob=.1)
G.add_edge(3, 5, act_prob=.2)
G.add_edge(4, 5, act_prob=.2)
G.add_edge(5, 6, act_prob=.6)
G.add_edge(6, 5, act_prob=.6)
G.add_edge(6, 4, act_prob=.3)
G.add_edge(6, 2, act_prob=.4)
n_A = 0.0
for i in range(TestIndependentCascade.run_times):
A = independent_cascade(G, [1], steps=1)
for layer in A:
n_A += len(layer)
assert_almost_equal(n_A / TestIndependentCascade.run_times,
1.7,
places=1)
n_A = 0.0
A = []
for i in range(TestIndependentCascade.run_times):
A = independent_cascade(G, [1], steps=2)
for layer in A:
n_A += len(layer)
assert_almost_equal(n_A / TestIndependentCascade.run_times,
2.16,
places=1)
G = networkx.DiGraph()
G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4)], act_prob=0.4)
n_A = 0.0
A = []
for i in range(TestIndependentCascade.run_times):
A = independent_cascade(G, [1])
for layer in A:
n_A += len(layer)
assert_almost_equal(n_A / TestIndependentCascade.run_times,
2.09,
places=1)
def test_independent_cascade_without_attribute(self):
G = networkx.DiGraph()
G.add_edges_from([(1,2), (1,3), (2,4), (3,4)])
n_A = 0.0
A = [ ]
for i in range(TestIndependentCascade.run_times):
A = independent_cascade(G, [1], steps=1)
for layer in A:
n_A += len(layer)
assert_almost_equal(n_A / TestIndependentCascade.run_times, 1.2, places=1)
def test_independent_cascade(self):
G = networkx.DiGraph()
G.add_edge(1,2,act_prob=.5)
G.add_edge(2,1,act_prob=.5)
G.add_edge(1,3,act_prob=.2)
G.add_edge(3,1,act_prob=.2)
G.add_edge(2,3,act_prob=.3)
G.add_edge(2,4,act_prob=.5)
G.add_edge(3,4,act_prob=.1)
G.add_edge(3,5,act_prob=.2)
G.add_edge(4,5,act_prob=.2)
G.add_edge(5,6,act_prob=.6)
G.add_edge(6,5,act_prob=.6)
G.add_edge(6,4,act_prob=.3)
G.add_edge(6,2,act_prob=.4)
n_A = 0.0
for i in range(TestIndependentCascade.run_times):
A = independent_cascade(G, [1], steps=1)
for layer in A:
n_A += len(layer)
assert_almost_equal(n_A / TestIndependentCascade.run_times, 1.7, places=1)
n_A = 0.0
A = [ ]
for i in range(TestIndependentCascade.run_times):
A = independent_cascade(G, [1], steps=2)
for layer in A:
n_A += len(layer)
assert_almost_equal(n_A / TestIndependentCascade.run_times, 2.16, places=1)
G = networkx.DiGraph()
G.add_edges_from([(1,2), (1,3), (2,4), (3,4)], act_prob=0.4)
n_A = 0.0
A = [ ]
for i in range(TestIndependentCascade.run_times):
A = independent_cascade(G, [1])
for layer in A:
n_A += len(layer)
assert_almost_equal(n_A / TestIndependentCascade.run_times, 2.09, places=1)
def test_independent_cascade_without_attribute(self):
G = networkx.DiGraph()
G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4)])
n_A = 0.0
A = []
for i in range(TestIndependentCascade.run_times):
A = independent_cascade(G, [1], steps=1)
for layer in A:
n_A += len(layer)
assert_almost_equal(n_A / TestIndependentCascade.run_times,
1.2,
places=1)
