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#!/usr/bin/env python
from nose.tools import *
from nose import SkipTest
import networkx
# Example from
# A. Langville and C. Meyer, "A survey of eigenvector methods of web
# information retrieval." http://citeseer.ist.psu.edu/713792.html
class TestPageRank:
def setUp(self):
G=networkx.DiGraph()
edges=[(1,2),(1,3),\
(3,1),(3,2),(3,5),\
(4,5),(4,6),\
(5,4),(5,6),\
(6,4)]
G.add_edges_from(edges)
self.G=G
self.G.pagerank=dict(zip(G,
[0.03721197,0.05395735,0.04150565,
0.37508082,0.20599833, 0.28624589]))
def test_pagerank(self):
G=self.G
p=networkx.pagerank(G,alpha=0.9,tol=1.e-08)
for n in G:
assert_almost_equal(p[n],G.pagerank[n],places=4)
def test_numpy_pagerank(self):
try:
import numpy
except ImportError:
raise SkipTest('numpy not available.')
G=self.G
p=networkx.pagerank_numpy(G,alpha=0.9)
for n in G:
assert_almost_equal(p[n],G.pagerank[n],places=4)
def test_google_matrix(self):
try:
import numpy.linalg
except ImportError:
raise SkipTest('numpy not available.')
G=self.G
M=networkx.google_matrix(G,alpha=0.9)
e,ev=numpy.linalg.eig(M.T)
p=numpy.array(ev[:,0]/ev[:,0].sum())[:,0]
for (a,b) in zip(p,self.G.pagerank.values()):
assert_almost_equal(a,b)
def test_scipy_pagerank(self):
G=self.G
try:
import scipy
except ImportError:
raise SkipTest('scipy not available.')
p=networkx.pagerank_scipy(G,alpha=0.9,tol=1.e-08)
for n in G:
assert_almost_equal(p[n],G.pagerank[n],places=4)
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