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from nose import SkipTest
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
class TestModularity(object):
numpy = 1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@classmethod
def setupClass(cls):
global numpy
global scipy
global assert_equal
global assert_almost_equal
try:
import numpy
import scipy
from numpy.testing import assert_equal, assert_almost_equal
except ImportError:
raise SkipTest('SciPy not available.')
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond". (Used for test_directed_laplacian)
self.DG = nx.DiGraph()
self.DG.add_edges_from(((1,2), (1,3), (3,1), (3,2), (3,5), (4,5), (4,6),
(5,4), (5,6), (6,4)))
def test_modularity(self):
"Modularity matrix"
B = numpy.matrix([[-1.125, 0.25 , 0.25 , 0.625, 0. ],
[ 0.25 , -0.5 , 0.5 , -0.25 , 0. ],
[ 0.25 , 0.5 , -0.5 , -0.25 , 0. ],
[ 0.625, -0.25 , -0.25 , -0.125, 0. ],
[ 0. , 0. , 0. , 0. , 0. ]])
permutation = [4, 0, 1, 2, 3]
assert_equal(nx.modularity_matrix(self.G), B)
assert_equal(nx.modularity_matrix(self.G, nodelist=permutation),
B[numpy.ix_(permutation, permutation)])
def test_directed_modularity(self):
"Directed Modularity matrix"
B = numpy.matrix([[-0.2, 0.6, 0.8, -0.4, -0.4, -0.4],
[ 0. , 0. , 0. , 0. , 0. , 0. ],
[ 0.7, 0.4, -0.3, -0.6, 0.4, -0.6],
[-0.2, -0.4, -0.2, -0.4, 0.6, 0.6],
[-0.2, -0.4, -0.2, 0.6, -0.4, 0.6],
[-0.1, -0.2, -0.1, 0.8, -0.2, -0.2]])
node_permutation = [5, 1, 2, 3, 4, 6]
idx_permutation = [4, 0, 1, 2, 3, 5]
assert_equal(nx.directed_modularity_matrix(self.DG), B)
assert_equal(nx.directed_modularity_matrix(self.DG, nodelist=node_permutation),
B[numpy.ix_(idx_permutation, idx_permutation)])
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