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import math
import pytest
np = pytest.importorskip("numpy")
pytest.importorskip("scipy")
import networkx as nx
class TestEigenvectorCentrality:
def test_K5(self):
"""Eigenvector centrality: K5"""
G = nx.complete_graph(5)
b = nx.eigenvector_centrality(G)
v = math.sqrt(1 / 5.0)
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
nstart = {n: 1 for n in G}
b = nx.eigenvector_centrality(G, nstart=nstart)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
b = nx.eigenvector_centrality_numpy(G)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-3)
def test_P3(self):
"""Eigenvector centrality: P3"""
G = nx.path_graph(3)
b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
b = nx.eigenvector_centrality_numpy(G)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-4)
b = nx.eigenvector_centrality(G)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-4)
def test_P3_unweighted(self):
"""Eigenvector centrality: P3"""
G = nx.path_graph(3)
b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
b = nx.eigenvector_centrality_numpy(G, weight=None)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-4)
def test_maxiter(self):
with pytest.raises(nx.PowerIterationFailedConvergence):
G = nx.path_graph(3)
b = nx.eigenvector_centrality(G, max_iter=0)
class TestEigenvectorCentralityDirected:
@classmethod
def setup_class(cls):
G = nx.DiGraph()
edges = [
(1, 2),
(1, 3),
(2, 4),
(3, 2),
(3, 5),
(4, 2),
(4, 5),
(4, 6),
(5, 6),
(5, 7),
(5, 8),
(6, 8),
(7, 1),
(7, 5),
(7, 8),
(8, 6),
(8, 7),
]
G.add_edges_from(edges, weight=2.0)
cls.G = G.reverse()
cls.G.evc = [
0.25368793,
0.19576478,
0.32817092,
0.40430835,
0.48199885,
0.15724483,
0.51346196,
0.32475403,
]
H = nx.DiGraph()
edges = [
(1, 2),
(1, 3),
(2, 4),
(3, 2),
(3, 5),
(4, 2),
(4, 5),
(4, 6),
(5, 6),
(5, 7),
(5, 8),
(6, 8),
(7, 1),
(7, 5),
(7, 8),
(8, 6),
(8, 7),
]
G.add_edges_from(edges)
cls.H = G.reverse()
cls.H.evc = [
0.25368793,
0.19576478,
0.32817092,
0.40430835,
0.48199885,
0.15724483,
0.51346196,
0.32475403,
]
def test_eigenvector_centrality_weighted(self):
G = self.G
p = nx.eigenvector_centrality(G)
for (a, b) in zip(list(p.values()), self.G.evc):
assert a == pytest.approx(b, abs=1e-4)
def test_eigenvector_centrality_weighted_numpy(self):
G = self.G
p = nx.eigenvector_centrality_numpy(G)
for (a, b) in zip(list(p.values()), self.G.evc):
assert a == pytest.approx(b, abs=1e-7)
def test_eigenvector_centrality_unweighted(self):
G = self.H
p = nx.eigenvector_centrality(G)
for (a, b) in zip(list(p.values()), self.G.evc):
assert a == pytest.approx(b, abs=1e-4)
def test_eigenvector_centrality_unweighted_numpy(self):
G = self.H
p = nx.eigenvector_centrality_numpy(G)
for (a, b) in zip(list(p.values()), self.G.evc):
assert a == pytest.approx(b, abs=1e-7)
class TestEigenvectorCentralityExceptions:
def test_multigraph(self):
with pytest.raises(nx.NetworkXException):
e = nx.eigenvector_centrality(nx.MultiGraph())
def test_multigraph_numpy(self):
with pytest.raises(nx.NetworkXException):
e = nx.eigenvector_centrality_numpy(nx.MultiGraph())
def test_empty(self):
with pytest.raises(nx.NetworkXException):
e = nx.eigenvector_centrality(nx.Graph())
def test_empty_numpy(self):
with pytest.raises(nx.NetworkXException):
e = nx.eigenvector_centrality_numpy(nx.Graph())
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