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
168
169
170
171
172
173
174
175
|
import pytest
import networkx as nx
from networkx.algorithms import bipartite
class TestBipartiteCentrality:
@classmethod
def setup_class(cls):
cls.P4 = nx.path_graph(4)
cls.K3 = nx.complete_bipartite_graph(3, 3)
cls.C4 = nx.cycle_graph(4)
cls.davis = nx.davis_southern_women_graph()
cls.top_nodes = [
n for n, d in cls.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 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 d == answer
d = bipartite.degree_centrality(self.C4, [0, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert 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 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 c == answer
c = bipartite.betweenness_centrality(self.C4, [0, 2])
answer = {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
assert 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 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 c == answer
c = bipartite.closeness_centrality(self.C4, [0, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert c == answer
G = nx.Graph()
G.add_node(0)
G.add_node(1)
c = bipartite.closeness_centrality(G, [0])
assert c == {1: 0.0}
c = bipartite.closeness_centrality(G, [1])
assert 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 value == pytest.approx(deg[node], abs=1e-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 value == pytest.approx(bet[node], abs=1e-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 value == pytest.approx(clos[node], abs=1e-2)
|
