/usr/lib/python2.7/dist-packages/networkx/algorithms/bipartite/tests/test_centrality.py is in python-networkx 1.8.1-0ubuntu3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | from nose.tools import *
import networkx as nx
from networkx.algorithms import bipartite
class TestBipartiteCentrality(object):
def setUp(self):
self.P4 = nx.path_graph(4)
self.K3 = nx.complete_bipartite_graph(3,3)
self.C4 = nx.cycle_graph(4)
self.davis = nx.davis_southern_women_graph()
self.top_nodes = [n for n,d in self.davis.nodes(data=True)
if d['bipartite']==0]
def test_degree_centrality(self):
d = bipartite.degree_centrality(self.P4, [1,3])
answer = {0: 0.5, 1: 1.0, 2: 1.0, 3: 0.5}
assert_equal(d, answer)
d = bipartite.degree_centrality(self.K3, [0,1,2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
assert_equal(d, answer)
d = bipartite.degree_centrality(self.C4, [0,2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert_equal(d,answer)
def test_betweenness_centrality(self):
c = bipartite.betweenness_centrality(self.P4, [1,3])
answer = {0: 0.0, 1: 1.0, 2: 1.0, 3: 0.0}
assert_equal(c, answer)
c = bipartite.betweenness_centrality(self.K3, [0,1,2])
answer = {0: 0.125, 1: 0.125, 2: 0.125, 3: 0.125, 4: 0.125, 5: 0.125}
assert_equal(c, answer)
c = bipartite.betweenness_centrality(self.C4, [0,2])
answer = {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
assert_equal(c, answer)
def test_closeness_centrality(self):
c = bipartite.closeness_centrality(self.P4, [1,3])
answer = {0: 2.0/3, 1: 1.0, 2: 1.0, 3:2.0/3}
assert_equal(c, answer)
c = bipartite.closeness_centrality(self.K3, [0,1,2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
assert_equal(c, answer)
c = bipartite.closeness_centrality(self.C4, [0,2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert_equal(c, answer)
G = nx.Graph()
G.add_node(0)
G.add_node(1)
c = bipartite.closeness_centrality(G, [0])
assert_equal(c, {1: 0.0})
c = bipartite.closeness_centrality(G, [1])
assert_equal(c, {1: 0.0})
def test_davis_degree_centrality(self):
G = self.davis
deg = bipartite.degree_centrality(G, self.top_nodes)
answer = {'E8':0.78,
'E9':0.67,
'E7':0.56,
'Nora Fayette':0.57,
'Evelyn Jefferson':0.57,
'Theresa Anderson':0.57,
'E6':0.44,
'Sylvia Avondale':0.50,
'Laura Mandeville':0.50,
'Brenda Rogers':0.50,
'Katherina Rogers':0.43,
'E5':0.44,
'Helen Lloyd':0.36,
'E3':0.33,
'Ruth DeSand':0.29,
'Verne Sanderson':0.29,
'E12':0.33,
'Myra Liddel':0.29,
'E11':0.22,
'Eleanor Nye':0.29,
'Frances Anderson':0.29,
'Pearl Oglethorpe':0.21,
'E4':0.22,
'Charlotte McDowd':0.29,
'E10':0.28,
'Olivia Carleton':0.14,
'Flora Price':0.14,
'E2':0.17,
'E1':0.17,
'Dorothy Murchison':0.14,
'E13':0.17,
'E14':0.17}
for node, value in answer.items():
assert_almost_equal(value, deg[node], places=2)
def test_davis_betweenness_centrality(self):
G = self.davis
bet = bipartite.betweenness_centrality(G, self.top_nodes)
answer = {'E8':0.24,
'E9':0.23,
'E7':0.13,
'Nora Fayette':0.11,
'Evelyn Jefferson':0.10,
'Theresa Anderson':0.09,
'E6':0.07,
'Sylvia Avondale':0.07,
'Laura Mandeville':0.05,
'Brenda Rogers':0.05,
'Katherina Rogers':0.05,
'E5':0.04,
'Helen Lloyd':0.04,
'E3':0.02,
'Ruth DeSand':0.02,
'Verne Sanderson':0.02,
'E12':0.02,
'Myra Liddel':0.02,
'E11':0.02,
'Eleanor Nye':0.01,
'Frances Anderson':0.01,
'Pearl Oglethorpe':0.01,
'E4':0.01,
'Charlotte McDowd':0.01,
'E10':0.01,
'Olivia Carleton':0.01,
'Flora Price':0.01,
'E2':0.00,
'E1':0.00,
'Dorothy Murchison':0.00,
'E13':0.00,
'E14':0.00}
for node, value in answer.items():
assert_almost_equal(value, bet[node], places=2)
def test_davis_closeness_centrality(self):
G = self.davis
clos = bipartite.closeness_centrality(G, self.top_nodes)
answer = {'E8':0.85,
'E9':0.79,
'E7':0.73,
'Nora Fayette':0.80,
'Evelyn Jefferson':0.80,
'Theresa Anderson':0.80,
'E6':0.69,
'Sylvia Avondale':0.77,
'Laura Mandeville':0.73,
'Brenda Rogers':0.73,
'Katherina Rogers':0.73,
'E5':0.59,
'Helen Lloyd':0.73,
'E3':0.56,
'Ruth DeSand':0.71,
'Verne Sanderson':0.71,
'E12':0.56,
'Myra Liddel':0.69,
'E11':0.54,
'Eleanor Nye':0.67,
'Frances Anderson':0.67,
'Pearl Oglethorpe':0.67,
'E4':0.54,
'Charlotte McDowd':0.60,
'E10':0.55,
'Olivia Carleton':0.59,
'Flora Price':0.59,
'E2':0.52,
'E1':0.52,
'Dorothy Murchison':0.65,
'E13':0.52,
'E14':0.52}
for node, value in answer.items():
assert_almost_equal(value, clos[node], places=2)
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