def test_K5(self):
"""Katz centrality: K5"""
G = nx.complete_graph(5)
alpha = 0.1
b = nx.katz_centrality(G, alpha)
v = math.sqrt(1 / 5.0)
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
nstart = dict([(n, 1) for n in G])
b = nx.katz_centrality(G, alpha, nstart=nstart)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def test_maxiter(self):
with pytest.raises(nx.PowerIterationFailedConvergence):
alpha = 0.1
G = nx.path_graph(3)
max_iter = 0
try:
b = nx.katz_centrality(G, alpha, max_iter=max_iter)
except nx.NetworkXError as e:
assert str(
max_iter) in e.args[0], "max_iter value not in error msg"
raise # So that the decorater sees the exception.
def test_beta_as_dict(self):
alpha = 0.1
beta = {0: 1.0, 1: 1.0, 2: 1.0}
b_answer = {
0: 0.5598852584152165,
1: 0.6107839182711449,
2: 0.5598852584152162
}
G = nx.path_graph(3)
b = nx.katz_centrality(G, alpha, beta)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n], places=4)
def test_P3(self):
"""Katz centrality: P3"""
alpha = 0.1
G = nx.path_graph(3)
b_answer = {
0: 0.5598852584152165,
1: 0.6107839182711449,
2: 0.5598852584152162
}
b = nx.katz_centrality(G, alpha)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n], places=4)
def test_K5_unweighted(self):
"""Katz centrality: K5"""
G = nx.complete_graph(5)
alpha = 0.1
b = nx.katz_centrality(G, alpha, weight=None)
v = math.sqrt(1 / 5.0)
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
nstart = dict([(n, 1) for n in G])
b = nx.eigenvector_centrality_numpy(G, weight=None)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n], places=3)
def test_multiple_alpha(self):
alpha_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
for alpha in alpha_list:
b_answer = {
0.1: {
0: 0.5598852584152165,
1: 0.6107839182711449,
2: 0.5598852584152162,
},
0.2: {
0: 0.5454545454545454,
1: 0.6363636363636365,
2: 0.5454545454545454,
},
0.3: {
0: 0.5333964609104419,
1: 0.6564879518897746,
2: 0.5333964609104419,
},
0.4: {
0: 0.5232045649263551,
1: 0.6726915834767423,
2: 0.5232045649263551,
},
0.5: {
0: 0.5144957746691622,
1: 0.6859943117075809,
2: 0.5144957746691622,
},
0.6: {
0: 0.5069794004195823,
1: 0.6970966755769258,
2: 0.5069794004195823,
},
}
G = nx.path_graph(3)
b = nx.katz_centrality(G, alpha)
for n in sorted(G):
assert almost_equal(b[n], b_answer[alpha][n], places=4)
def test_katz_centrality_unweighted(self):
H = self.H
alpha = self.H.alpha
p = nx.katz_centrality(H, alpha, weight="weight")
for (a, b) in zip(list(p.values()), self.H.evc):
assert almost_equal(a, b)
def test_katz_centrality_weighted(self):
G = self.G
alpha = self.G.alpha
p = nx.katz_centrality(G, alpha, weight="weight")
for (a, b) in zip(list(p.values()), self.G.evc):
assert almost_equal(a, b)
def test_empty(self):
e = nx.katz_centrality(nx.Graph(), 0.1)
assert e == {}
def test_multigraph(self):
with pytest.raises(nx.NetworkXException):
e = nx.katz_centrality(nx.MultiGraph(), 0.1)
def test_bad_beta_numbe(self):
with pytest.raises(nx.NetworkXException):
G = nx.Graph([(0, 1)])
e = nx.katz_centrality(G, 0.1, beta="foo")
def test_bad_beta(self):
with pytest.raises(nx.NetworkXException):
G = nx.Graph([(0, 1)])
beta = {0: 77}
e = nx.katz_centrality(G, 0.1, beta=beta)