66 lines
2.9 KiB
Python
66 lines
2.9 KiB
Python
import pytest
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numpy = pytest.importorskip('numpy')
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npt = pytest.importorskip('numpy.testing')
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scipy = pytest.importorskip('scipy')
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import networkx as nx
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from networkx.generators.degree_seq import havel_hakimi_graph
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class TestModularity(object):
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@classmethod
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def setup_class(cls):
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deg = [3, 2, 2, 1, 0]
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cls.G = havel_hakimi_graph(deg)
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# Graph used as an example in Sec. 4.1 of Langville and Meyer,
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# "Google's PageRank and Beyond". (Used for test_directed_laplacian)
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cls.DG = nx.DiGraph()
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cls.DG.add_edges_from(((1, 2), (1, 3), (3, 1), (3, 2), (3, 5), (4, 5), (4, 6),
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(5, 4), (5, 6), (6, 4)))
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def test_modularity(self):
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"Modularity matrix"
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B = numpy.matrix([[-1.125, 0.25, 0.25, 0.625, 0.],
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[0.25, -0.5, 0.5, -0.25, 0.],
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[0.25, 0.5, -0.5, -0.25, 0.],
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[0.625, -0.25, -0.25, -0.125, 0.],
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[0., 0., 0., 0., 0.]])
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permutation = [4, 0, 1, 2, 3]
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npt.assert_equal(nx.modularity_matrix(self.G), B)
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npt.assert_equal(nx.modularity_matrix(self.G, nodelist=permutation),
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B[numpy.ix_(permutation, permutation)])
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def test_modularity_weight(self):
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"Modularity matrix with weights"
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B = numpy.matrix([[-1.125, 0.25, 0.25, 0.625, 0.],
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[0.25, -0.5, 0.5, -0.25, 0.],
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[0.25, 0.5, -0.5, -0.25, 0.],
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[0.625, -0.25, -0.25, -0.125, 0.],
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[0., 0., 0., 0., 0.]])
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G_weighted = self.G.copy()
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for n1, n2 in G_weighted.edges():
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G_weighted.edges[n1, n2]["weight"] = 0.5
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# The following test would fail in networkx 1.1
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npt.assert_equal(nx.modularity_matrix(G_weighted), B)
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# The following test that the modularity matrix get rescaled accordingly
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npt.assert_equal(nx.modularity_matrix(G_weighted, weight="weight"), 0.5 * B)
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def test_directed_modularity(self):
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"Directed Modularity matrix"
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B = numpy.matrix([[-0.2, 0.6, 0.8, -0.4, -0.4, -0.4],
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[0., 0., 0., 0., 0., 0.],
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[0.7, 0.4, -0.3, -0.6, 0.4, -0.6],
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[-0.2, -0.4, -0.2, -0.4, 0.6, 0.6],
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[-0.2, -0.4, -0.2, 0.6, -0.4, 0.6],
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[-0.1, -0.2, -0.1, 0.8, -0.2, -0.2]])
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node_permutation = [5, 1, 2, 3, 4, 6]
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idx_permutation = [4, 0, 1, 2, 3, 5]
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mm = nx.directed_modularity_matrix(self.DG, nodelist=sorted(self.DG))
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npt.assert_equal(mm, B)
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npt.assert_equal(nx.directed_modularity_matrix(self.DG,
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nodelist=node_permutation),
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B[numpy.ix_(idx_permutation, idx_permutation)])
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