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#!/usr/bin/env python
import math
from nose import SkipTest
from nose.tools import *
import networkx
class TestEigenvectorCentrality(object):
numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@classmethod
def setupClass(cls):
global np
try:
import numpy as np
import scipy
except ImportError:
raise SkipTest('SciPy not available.')
def test_K5(self):
"""Eigenvector centrality: K5"""
G=networkx.complete_graph(5)
b=networkx.eigenvector_centrality(G)
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=networkx.eigenvector_centrality(G,nstart=nstart)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
b=networkx.eigenvector_centrality_numpy(G)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n],places=3)
def test_P3(self):
"""Eigenvector centrality: P3"""
G=networkx.path_graph(3)
b_answer={0: 0.5, 1: 0.7071, 2: 0.5}
b=networkx.eigenvector_centrality_numpy(G)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n],places=4)
def test_P3_unweighted(self):
"""Eigenvector centrality: P3"""
G=networkx.path_graph(3)
b_answer={0: 0.5, 1: 0.7071, 2: 0.5}
b=networkx.eigenvector_centrality_numpy(G, weight=None)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n],places=4)
@raises(networkx.NetworkXError)
def test_maxiter(self):
G=networkx.path_graph(3)
b=networkx.eigenvector_centrality(G,max_iter=0)
class TestEigenvectorCentralityDirected(object):
numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@classmethod
def setupClass(cls):
global np
try:
import numpy as np
import scipy
except ImportError:
raise SkipTest('SciPy not available.')
def setUp(self):
G=networkx.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)
self.G=G.reverse()
self.G.evc=[0.25368793, 0.19576478, 0.32817092, 0.40430835,
0.48199885, 0.15724483, 0.51346196, 0.32475403]
H=networkx.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)
self.H=G.reverse()
self.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=networkx.eigenvector_centrality(G)
for (a,b) in zip(list(p.values()),self.G.evc):
assert_almost_equal(a,b,places=4)
def test_eigenvector_centrality_weighted_numpy(self):
G=self.G
p=networkx.eigenvector_centrality_numpy(G)
for (a,b) in zip(list(p.values()),self.G.evc):
assert_almost_equal(a,b)
def test_eigenvector_centrality_unweighted(self):
G=self.H
p=networkx.eigenvector_centrality(G)
for (a,b) in zip(list(p.values()),self.G.evc):
assert_almost_equal(a,b,places=4)
def test_eigenvector_centrality_unweighted_numpy(self):
G=self.H
p=networkx.eigenvector_centrality_numpy(G)
for (a,b) in zip(list(p.values()),self.G.evc):
assert_almost_equal(a,b)
class TestEigenvectorCentralityExceptions(object):
numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@classmethod
def setupClass(cls):
global np
try:
import numpy as np
import scipy
except ImportError:
raise SkipTest('SciPy not available.')
numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@raises(networkx.NetworkXException)
def test_multigraph(self):
e = networkx.eigenvector_centrality(networkx.MultiGraph())
@raises(networkx.NetworkXException)
def test_multigraph_numpy(self):
e = networkx.eigenvector_centrality_numpy(networkx.MultiGraph())
@raises(networkx.NetworkXException)
def test_empty(self):
e = networkx.eigenvector_centrality(networkx.Graph())
@raises(networkx.NetworkXException)
def test_empty_numpy(self):
e = networkx.eigenvector_centrality_numpy(networkx.Graph())
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