Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/centrality/tests/test_betweenness_centrality.py @ 0:4f3585e2f14b draft default tip
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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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1 import networkx as nx |
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2 from networkx.testing import almost_equal |
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3 |
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4 |
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5 def weighted_G(): |
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6 G = nx.Graph() |
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7 G.add_edge(0, 1, weight=3) |
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8 G.add_edge(0, 2, weight=2) |
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9 G.add_edge(0, 3, weight=6) |
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10 G.add_edge(0, 4, weight=4) |
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11 G.add_edge(1, 3, weight=5) |
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12 G.add_edge(1, 5, weight=5) |
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13 G.add_edge(2, 4, weight=1) |
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14 G.add_edge(3, 4, weight=2) |
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15 G.add_edge(3, 5, weight=1) |
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16 G.add_edge(4, 5, weight=4) |
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17 return G |
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18 |
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19 |
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20 class TestBetweennessCentrality: |
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21 def test_K5(self): |
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22 """Betweenness centrality: K5""" |
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23 G = nx.complete_graph(5) |
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24 b = nx.betweenness_centrality(G, weight=None, normalized=False) |
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25 b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0} |
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26 for n in sorted(G): |
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27 assert almost_equal(b[n], b_answer[n]) |
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28 |
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29 def test_K5_endpoints(self): |
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30 """Betweenness centrality: K5 endpoints""" |
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31 G = nx.complete_graph(5) |
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32 b = nx.betweenness_centrality(G, weight=None, normalized=False, endpoints=True) |
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33 b_answer = {0: 4.0, 1: 4.0, 2: 4.0, 3: 4.0, 4: 4.0} |
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34 for n in sorted(G): |
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35 assert almost_equal(b[n], b_answer[n]) |
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36 # normalized = True case |
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37 b = nx.betweenness_centrality(G, weight=None, normalized=True, endpoints=True) |
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38 b_answer = {0: 0.4, 1: 0.4, 2: 0.4, 3: 0.4, 4: 0.4} |
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39 for n in sorted(G): |
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40 assert almost_equal(b[n], b_answer[n]) |
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41 |
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42 def test_P3_normalized(self): |
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43 """Betweenness centrality: P3 normalized""" |
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44 G = nx.path_graph(3) |
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45 b = nx.betweenness_centrality(G, weight=None, normalized=True) |
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46 b_answer = {0: 0.0, 1: 1.0, 2: 0.0} |
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47 for n in sorted(G): |
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48 assert almost_equal(b[n], b_answer[n]) |
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49 |
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50 def test_P3(self): |
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51 """Betweenness centrality: P3""" |
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52 G = nx.path_graph(3) |
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53 b_answer = {0: 0.0, 1: 1.0, 2: 0.0} |
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54 b = nx.betweenness_centrality(G, weight=None, normalized=False) |
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55 for n in sorted(G): |
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56 assert almost_equal(b[n], b_answer[n]) |
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57 |
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58 def test_sample_from_P3(self): |
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59 G = nx.path_graph(3) |
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60 b_answer = {0: 0.0, 1: 1.0, 2: 0.0} |
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61 b = nx.betweenness_centrality(G, k=3, weight=None, normalized=False, seed=1) |
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62 for n in sorted(G): |
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63 assert almost_equal(b[n], b_answer[n]) |
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64 b = nx.betweenness_centrality(G, k=2, weight=None, normalized=False, seed=1) |
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65 # python versions give different results with same seed |
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66 b_approx1 = {0: 0.0, 1: 1.5, 2: 0.0} |
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67 b_approx2 = {0: 0.0, 1: 0.75, 2: 0.0} |
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68 for n in sorted(G): |
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69 assert b[n] in (b_approx1[n], b_approx2[n]) |
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70 |
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71 def test_P3_endpoints(self): |
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72 """Betweenness centrality: P3 endpoints""" |
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73 G = nx.path_graph(3) |
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74 b_answer = {0: 2.0, 1: 3.0, 2: 2.0} |
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75 b = nx.betweenness_centrality(G, weight=None, normalized=False, endpoints=True) |
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76 for n in sorted(G): |
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77 assert almost_equal(b[n], b_answer[n]) |
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78 # normalized = True case |
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79 b_answer = {0: 2 / 3, 1: 1.0, 2: 2 / 3} |
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80 b = nx.betweenness_centrality(G, weight=None, normalized=True, endpoints=True) |
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81 for n in sorted(G): |
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82 assert almost_equal(b[n], b_answer[n]) |
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83 |
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84 def test_krackhardt_kite_graph(self): |
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85 """Betweenness centrality: Krackhardt kite graph""" |
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86 G = nx.krackhardt_kite_graph() |
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87 b_answer = { |
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88 0: 1.667, |
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89 1: 1.667, |
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90 2: 0.000, |
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91 3: 7.333, |
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92 4: 0.000, |
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93 5: 16.667, |
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94 6: 16.667, |
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95 7: 28.000, |
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96 8: 16.000, |
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97 9: 0.000, |
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98 } |
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99 for b in b_answer: |
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100 b_answer[b] /= 2 |
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101 b = nx.betweenness_centrality(G, weight=None, normalized=False) |
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102 for n in sorted(G): |
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103 assert almost_equal(b[n], b_answer[n], places=3) |
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104 |
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105 def test_krackhardt_kite_graph_normalized(self): |
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106 """Betweenness centrality: Krackhardt kite graph normalized""" |
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107 G = nx.krackhardt_kite_graph() |
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108 b_answer = { |
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109 0: 0.023, |
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110 1: 0.023, |
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111 2: 0.000, |
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112 3: 0.102, |
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113 4: 0.000, |
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114 5: 0.231, |
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115 6: 0.231, |
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116 7: 0.389, |
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117 8: 0.222, |
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118 9: 0.000, |
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119 } |
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120 b = nx.betweenness_centrality(G, weight=None, normalized=True) |
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121 for n in sorted(G): |
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122 assert almost_equal(b[n], b_answer[n], places=3) |
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123 |
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124 def test_florentine_families_graph(self): |
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125 """Betweenness centrality: Florentine families graph""" |
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126 G = nx.florentine_families_graph() |
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127 b_answer = { |
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128 "Acciaiuoli": 0.000, |
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129 "Albizzi": 0.212, |
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130 "Barbadori": 0.093, |
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131 "Bischeri": 0.104, |
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132 "Castellani": 0.055, |
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133 "Ginori": 0.000, |
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134 "Guadagni": 0.255, |
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135 "Lamberteschi": 0.000, |
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136 "Medici": 0.522, |
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137 "Pazzi": 0.000, |
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138 "Peruzzi": 0.022, |
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139 "Ridolfi": 0.114, |
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140 "Salviati": 0.143, |
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141 "Strozzi": 0.103, |
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142 "Tornabuoni": 0.092, |
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143 } |
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144 |
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145 b = nx.betweenness_centrality(G, weight=None, normalized=True) |
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146 for n in sorted(G): |
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147 assert almost_equal(b[n], b_answer[n], places=3) |
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148 |
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149 def test_les_miserables_graph(self): |
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150 """Betweenness centrality: Les Miserables graph""" |
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151 G = nx.les_miserables_graph() |
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152 b_answer = { |
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153 "Napoleon": 0.000, |
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154 "Myriel": 0.177, |
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155 "MlleBaptistine": 0.000, |
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156 "MmeMagloire": 0.000, |
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157 "CountessDeLo": 0.000, |
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158 "Geborand": 0.000, |
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159 "Champtercier": 0.000, |
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160 "Cravatte": 0.000, |
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161 "Count": 0.000, |
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162 "OldMan": 0.000, |
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163 "Valjean": 0.570, |
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164 "Labarre": 0.000, |
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165 "Marguerite": 0.000, |
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166 "MmeDeR": 0.000, |
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167 "Isabeau": 0.000, |
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168 "Gervais": 0.000, |
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169 "Listolier": 0.000, |
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170 "Tholomyes": 0.041, |
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171 "Fameuil": 0.000, |
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172 "Blacheville": 0.000, |
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173 "Favourite": 0.000, |
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174 "Dahlia": 0.000, |
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175 "Zephine": 0.000, |
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176 "Fantine": 0.130, |
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177 "MmeThenardier": 0.029, |
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178 "Thenardier": 0.075, |
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179 "Cosette": 0.024, |
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180 "Javert": 0.054, |
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181 "Fauchelevent": 0.026, |
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182 "Bamatabois": 0.008, |
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183 "Perpetue": 0.000, |
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184 "Simplice": 0.009, |
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185 "Scaufflaire": 0.000, |
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186 "Woman1": 0.000, |
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187 "Judge": 0.000, |
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188 "Champmathieu": 0.000, |
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189 "Brevet": 0.000, |
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190 "Chenildieu": 0.000, |
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191 "Cochepaille": 0.000, |
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192 "Pontmercy": 0.007, |
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193 "Boulatruelle": 0.000, |
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194 "Eponine": 0.011, |
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195 "Anzelma": 0.000, |
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196 "Woman2": 0.000, |
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197 "MotherInnocent": 0.000, |
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198 "Gribier": 0.000, |
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199 "MmeBurgon": 0.026, |
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200 "Jondrette": 0.000, |
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201 "Gavroche": 0.165, |
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202 "Gillenormand": 0.020, |
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203 "Magnon": 0.000, |
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204 "MlleGillenormand": 0.048, |
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205 "MmePontmercy": 0.000, |
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206 "MlleVaubois": 0.000, |
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207 "LtGillenormand": 0.000, |
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208 "Marius": 0.132, |
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209 "BaronessT": 0.000, |
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210 "Mabeuf": 0.028, |
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211 "Enjolras": 0.043, |
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212 "Combeferre": 0.001, |
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213 "Prouvaire": 0.000, |
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214 "Feuilly": 0.001, |
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215 "Courfeyrac": 0.005, |
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216 "Bahorel": 0.002, |
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217 "Bossuet": 0.031, |
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218 "Joly": 0.002, |
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219 "Grantaire": 0.000, |
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220 "MotherPlutarch": 0.000, |
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221 "Gueulemer": 0.005, |
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222 "Babet": 0.005, |
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223 "Claquesous": 0.005, |
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224 "Montparnasse": 0.004, |
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225 "Toussaint": 0.000, |
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226 "Child1": 0.000, |
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227 "Child2": 0.000, |
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228 "Brujon": 0.000, |
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229 "MmeHucheloup": 0.000, |
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230 } |
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231 |
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232 b = nx.betweenness_centrality(G, weight=None, normalized=True) |
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233 for n in sorted(G): |
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234 assert almost_equal(b[n], b_answer[n], places=3) |
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235 |
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236 def test_ladder_graph(self): |
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237 """Betweenness centrality: Ladder graph""" |
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238 G = nx.Graph() # ladder_graph(3) |
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239 G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)]) |
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240 b_answer = {0: 1.667, 1: 1.667, 2: 6.667, 3: 6.667, 4: 1.667, 5: 1.667} |
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241 for b in b_answer: |
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242 b_answer[b] /= 2 |
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243 b = nx.betweenness_centrality(G, weight=None, normalized=False) |
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244 for n in sorted(G): |
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245 assert almost_equal(b[n], b_answer[n], places=3) |
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246 |
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247 def test_disconnected_path(self): |
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248 """Betweenness centrality: disconnected path""" |
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249 G = nx.Graph() |
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250 nx.add_path(G, [0, 1, 2]) |
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251 nx.add_path(G, [3, 4, 5, 6]) |
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252 b_answer = {0: 0, 1: 1, 2: 0, 3: 0, 4: 2, 5: 2, 6: 0} |
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253 b = nx.betweenness_centrality(G, weight=None, normalized=False) |
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254 for n in sorted(G): |
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255 assert almost_equal(b[n], b_answer[n]) |
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256 |
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257 def test_disconnected_path_endpoints(self): |
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258 """Betweenness centrality: disconnected path endpoints""" |
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259 G = nx.Graph() |
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260 nx.add_path(G, [0, 1, 2]) |
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261 nx.add_path(G, [3, 4, 5, 6]) |
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262 b_answer = {0: 2, 1: 3, 2: 2, 3: 3, 4: 5, 5: 5, 6: 3} |
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263 b = nx.betweenness_centrality(G, weight=None, normalized=False, endpoints=True) |
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264 for n in sorted(G): |
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265 assert almost_equal(b[n], b_answer[n]) |
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266 # normalized = True case |
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267 b = nx.betweenness_centrality(G, weight=None, normalized=True, endpoints=True) |
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268 for n in sorted(G): |
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269 assert almost_equal(b[n], b_answer[n] / 21) |
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270 |
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271 def test_directed_path(self): |
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272 """Betweenness centrality: directed path""" |
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273 G = nx.DiGraph() |
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274 nx.add_path(G, [0, 1, 2]) |
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275 b = nx.betweenness_centrality(G, weight=None, normalized=False) |
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276 b_answer = {0: 0.0, 1: 1.0, 2: 0.0} |
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277 for n in sorted(G): |
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278 assert almost_equal(b[n], b_answer[n]) |
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279 |
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280 def test_directed_path_normalized(self): |
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281 """Betweenness centrality: directed path normalized""" |
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282 G = nx.DiGraph() |
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283 nx.add_path(G, [0, 1, 2]) |
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284 b = nx.betweenness_centrality(G, weight=None, normalized=True) |
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285 b_answer = {0: 0.0, 1: 0.5, 2: 0.0} |
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286 for n in sorted(G): |
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287 assert almost_equal(b[n], b_answer[n]) |
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288 |
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289 |
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290 class TestWeightedBetweennessCentrality: |
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291 def test_K5(self): |
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292 """Weighted betweenness centrality: K5""" |
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293 G = nx.complete_graph(5) |
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294 b = nx.betweenness_centrality(G, weight="weight", normalized=False) |
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295 b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0} |
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296 for n in sorted(G): |
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297 assert almost_equal(b[n], b_answer[n]) |
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298 |
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299 def test_P3_normalized(self): |
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300 """Weighted betweenness centrality: P3 normalized""" |
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301 G = nx.path_graph(3) |
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302 b = nx.betweenness_centrality(G, weight="weight", normalized=True) |
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303 b_answer = {0: 0.0, 1: 1.0, 2: 0.0} |
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304 for n in sorted(G): |
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305 assert almost_equal(b[n], b_answer[n]) |
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306 |
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307 def test_P3(self): |
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308 """Weighted betweenness centrality: P3""" |
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309 G = nx.path_graph(3) |
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310 b_answer = {0: 0.0, 1: 1.0, 2: 0.0} |
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311 b = nx.betweenness_centrality(G, weight="weight", normalized=False) |
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312 for n in sorted(G): |
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313 assert almost_equal(b[n], b_answer[n]) |
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314 |
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315 def test_krackhardt_kite_graph(self): |
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316 """Weighted betweenness centrality: Krackhardt kite graph""" |
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317 G = nx.krackhardt_kite_graph() |
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318 b_answer = { |
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319 0: 1.667, |
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320 1: 1.667, |
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321 2: 0.000, |
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322 3: 7.333, |
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323 4: 0.000, |
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324 5: 16.667, |
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325 6: 16.667, |
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326 7: 28.000, |
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327 8: 16.000, |
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328 9: 0.000, |
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329 } |
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330 for b in b_answer: |
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331 b_answer[b] /= 2 |
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332 |
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333 b = nx.betweenness_centrality(G, weight="weight", normalized=False) |
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334 |
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335 for n in sorted(G): |
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336 assert almost_equal(b[n], b_answer[n], places=3) |
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337 |
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338 def test_krackhardt_kite_graph_normalized(self): |
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339 """Weighted betweenness centrality: |
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340 Krackhardt kite graph normalized |
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341 """ |
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342 G = nx.krackhardt_kite_graph() |
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343 b_answer = { |
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344 0: 0.023, |
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345 1: 0.023, |
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346 2: 0.000, |
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347 3: 0.102, |
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348 4: 0.000, |
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349 5: 0.231, |
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350 6: 0.231, |
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351 7: 0.389, |
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352 8: 0.222, |
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353 9: 0.000, |
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354 } |
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355 b = nx.betweenness_centrality(G, weight="weight", normalized=True) |
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356 |
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357 for n in sorted(G): |
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358 assert almost_equal(b[n], b_answer[n], places=3) |
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359 |
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360 def test_florentine_families_graph(self): |
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361 """Weighted betweenness centrality: |
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362 Florentine families graph""" |
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363 G = nx.florentine_families_graph() |
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364 b_answer = { |
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365 "Acciaiuoli": 0.000, |
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366 "Albizzi": 0.212, |
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367 "Barbadori": 0.093, |
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368 "Bischeri": 0.104, |
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369 "Castellani": 0.055, |
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370 "Ginori": 0.000, |
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371 "Guadagni": 0.255, |
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372 "Lamberteschi": 0.000, |
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373 "Medici": 0.522, |
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374 "Pazzi": 0.000, |
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375 "Peruzzi": 0.022, |
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376 "Ridolfi": 0.114, |
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377 "Salviati": 0.143, |
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378 "Strozzi": 0.103, |
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379 "Tornabuoni": 0.092, |
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380 } |
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381 |
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382 b = nx.betweenness_centrality(G, weight="weight", normalized=True) |
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383 for n in sorted(G): |
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384 assert almost_equal(b[n], b_answer[n], places=3) |
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385 |
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386 def test_les_miserables_graph(self): |
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387 """Weighted betweenness centrality: Les Miserables graph""" |
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388 G = nx.les_miserables_graph() |
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389 b_answer = { |
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390 "Napoleon": 0.000, |
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391 "Myriel": 0.177, |
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392 "MlleBaptistine": 0.000, |
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393 "MmeMagloire": 0.000, |
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394 "CountessDeLo": 0.000, |
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395 "Geborand": 0.000, |
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396 "Champtercier": 0.000, |
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397 "Cravatte": 0.000, |
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398 "Count": 0.000, |
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399 "OldMan": 0.000, |
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400 "Valjean": 0.454, |
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401 "Labarre": 0.000, |
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402 "Marguerite": 0.009, |
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403 "MmeDeR": 0.000, |
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404 "Isabeau": 0.000, |
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405 "Gervais": 0.000, |
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406 "Listolier": 0.000, |
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407 "Tholomyes": 0.066, |
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408 "Fameuil": 0.000, |
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409 "Blacheville": 0.000, |
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410 "Favourite": 0.000, |
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411 "Dahlia": 0.000, |
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412 "Zephine": 0.000, |
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413 "Fantine": 0.114, |
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414 "MmeThenardier": 0.046, |
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415 "Thenardier": 0.129, |
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416 "Cosette": 0.075, |
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417 "Javert": 0.193, |
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418 "Fauchelevent": 0.026, |
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419 "Bamatabois": 0.080, |
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420 "Perpetue": 0.000, |
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421 "Simplice": 0.001, |
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422 "Scaufflaire": 0.000, |
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423 "Woman1": 0.000, |
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424 "Judge": 0.000, |
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425 "Champmathieu": 0.000, |
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426 "Brevet": 0.000, |
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427 "Chenildieu": 0.000, |
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428 "Cochepaille": 0.000, |
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429 "Pontmercy": 0.023, |
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430 "Boulatruelle": 0.000, |
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431 "Eponine": 0.023, |
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432 "Anzelma": 0.000, |
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433 "Woman2": 0.000, |
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434 "MotherInnocent": 0.000, |
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435 "Gribier": 0.000, |
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436 "MmeBurgon": 0.026, |
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437 "Jondrette": 0.000, |
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438 "Gavroche": 0.285, |
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439 "Gillenormand": 0.024, |
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440 "Magnon": 0.005, |
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441 "MlleGillenormand": 0.036, |
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442 "MmePontmercy": 0.005, |
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443 "MlleVaubois": 0.000, |
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444 "LtGillenormand": 0.015, |
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445 "Marius": 0.072, |
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446 "BaronessT": 0.004, |
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447 "Mabeuf": 0.089, |
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448 "Enjolras": 0.003, |
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449 "Combeferre": 0.000, |
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450 "Prouvaire": 0.000, |
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451 "Feuilly": 0.004, |
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452 "Courfeyrac": 0.001, |
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453 "Bahorel": 0.007, |
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454 "Bossuet": 0.028, |
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455 "Joly": 0.000, |
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456 "Grantaire": 0.036, |
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457 "MotherPlutarch": 0.000, |
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458 "Gueulemer": 0.025, |
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459 "Babet": 0.015, |
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460 "Claquesous": 0.042, |
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461 "Montparnasse": 0.050, |
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462 "Toussaint": 0.011, |
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463 "Child1": 0.000, |
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464 "Child2": 0.000, |
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465 "Brujon": 0.002, |
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466 "MmeHucheloup": 0.034, |
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467 } |
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468 |
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469 b = nx.betweenness_centrality(G, weight="weight", normalized=True) |
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470 for n in sorted(G): |
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471 assert almost_equal(b[n], b_answer[n], places=3) |
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472 |
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473 def test_ladder_graph(self): |
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474 """Weighted betweenness centrality: Ladder graph""" |
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475 G = nx.Graph() # ladder_graph(3) |
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476 G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)]) |
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477 b_answer = {0: 1.667, 1: 1.667, 2: 6.667, 3: 6.667, 4: 1.667, 5: 1.667} |
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478 for b in b_answer: |
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479 b_answer[b] /= 2 |
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480 b = nx.betweenness_centrality(G, weight="weight", normalized=False) |
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481 for n in sorted(G): |
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482 assert almost_equal(b[n], b_answer[n], places=3) |
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483 |
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484 def test_G(self): |
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485 """Weighted betweenness centrality: G""" |
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486 G = weighted_G() |
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487 b_answer = {0: 2.0, 1: 0.0, 2: 4.0, 3: 3.0, 4: 4.0, 5: 0.0} |
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488 b = nx.betweenness_centrality(G, weight="weight", normalized=False) |
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489 for n in sorted(G): |
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490 assert almost_equal(b[n], b_answer[n]) |
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491 |
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492 def test_G2(self): |
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493 """Weighted betweenness centrality: G2""" |
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494 G = nx.DiGraph() |
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495 G.add_weighted_edges_from( |
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496 [ |
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497 ("s", "u", 10), |
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498 ("s", "x", 5), |
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499 ("u", "v", 1), |
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500 ("u", "x", 2), |
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501 ("v", "y", 1), |
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502 ("x", "u", 3), |
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503 ("x", "v", 5), |
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504 ("x", "y", 2), |
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505 ("y", "s", 7), |
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506 ("y", "v", 6), |
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507 ] |
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508 ) |
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509 |
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510 b_answer = {"y": 5.0, "x": 5.0, "s": 4.0, "u": 2.0, "v": 2.0} |
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511 |
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512 b = nx.betweenness_centrality(G, weight="weight", normalized=False) |
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513 for n in sorted(G): |
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514 assert almost_equal(b[n], b_answer[n]) |
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515 |
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516 |
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517 class TestEdgeBetweennessCentrality: |
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518 def test_K5(self): |
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519 """Edge betweenness centrality: K5""" |
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520 G = nx.complete_graph(5) |
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521 b = nx.edge_betweenness_centrality(G, weight=None, normalized=False) |
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522 b_answer = dict.fromkeys(G.edges(), 1) |
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523 for n in sorted(G.edges()): |
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524 assert almost_equal(b[n], b_answer[n]) |
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525 |
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526 def test_normalized_K5(self): |
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527 """Edge betweenness centrality: K5""" |
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528 G = nx.complete_graph(5) |
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529 b = nx.edge_betweenness_centrality(G, weight=None, normalized=True) |
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530 b_answer = dict.fromkeys(G.edges(), 1 / 10) |
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531 for n in sorted(G.edges()): |
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532 assert almost_equal(b[n], b_answer[n]) |
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533 |
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534 def test_C4(self): |
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535 """Edge betweenness centrality: C4""" |
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536 G = nx.cycle_graph(4) |
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537 b = nx.edge_betweenness_centrality(G, weight=None, normalized=True) |
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538 b_answer = {(0, 1): 2, (0, 3): 2, (1, 2): 2, (2, 3): 2} |
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539 for n in sorted(G.edges()): |
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540 assert almost_equal(b[n], b_answer[n] / 6) |
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541 |
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542 def test_P4(self): |
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543 """Edge betweenness centrality: P4""" |
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544 G = nx.path_graph(4) |
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545 b = nx.edge_betweenness_centrality(G, weight=None, normalized=False) |
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546 b_answer = {(0, 1): 3, (1, 2): 4, (2, 3): 3} |
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547 for n in sorted(G.edges()): |
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548 assert almost_equal(b[n], b_answer[n]) |
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549 |
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550 def test_normalized_P4(self): |
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551 """Edge betweenness centrality: P4""" |
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552 G = nx.path_graph(4) |
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553 b = nx.edge_betweenness_centrality(G, weight=None, normalized=True) |
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554 b_answer = {(0, 1): 3, (1, 2): 4, (2, 3): 3} |
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555 for n in sorted(G.edges()): |
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556 assert almost_equal(b[n], b_answer[n] / 6) |
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557 |
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558 def test_balanced_tree(self): |
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559 """Edge betweenness centrality: balanced tree""" |
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560 G = nx.balanced_tree(r=2, h=2) |
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561 b = nx.edge_betweenness_centrality(G, weight=None, normalized=False) |
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562 b_answer = {(0, 1): 12, (0, 2): 12, (1, 3): 6, (1, 4): 6, (2, 5): 6, (2, 6): 6} |
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563 for n in sorted(G.edges()): |
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564 assert almost_equal(b[n], b_answer[n]) |
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565 |
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566 |
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567 class TestWeightedEdgeBetweennessCentrality: |
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568 def test_K5(self): |
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569 """Edge betweenness centrality: K5""" |
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570 G = nx.complete_graph(5) |
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571 b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) |
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572 b_answer = dict.fromkeys(G.edges(), 1) |
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573 for n in sorted(G.edges()): |
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574 assert almost_equal(b[n], b_answer[n]) |
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575 |
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576 def test_C4(self): |
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577 """Edge betweenness centrality: C4""" |
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578 G = nx.cycle_graph(4) |
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579 b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) |
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580 b_answer = {(0, 1): 2, (0, 3): 2, (1, 2): 2, (2, 3): 2} |
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581 for n in sorted(G.edges()): |
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582 assert almost_equal(b[n], b_answer[n]) |
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583 |
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584 def test_P4(self): |
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585 """Edge betweenness centrality: P4""" |
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586 G = nx.path_graph(4) |
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587 b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) |
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588 b_answer = {(0, 1): 3, (1, 2): 4, (2, 3): 3} |
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589 for n in sorted(G.edges()): |
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590 assert almost_equal(b[n], b_answer[n]) |
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591 |
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592 def test_balanced_tree(self): |
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593 """Edge betweenness centrality: balanced tree""" |
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594 G = nx.balanced_tree(r=2, h=2) |
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595 b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) |
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596 b_answer = {(0, 1): 12, (0, 2): 12, (1, 3): 6, (1, 4): 6, (2, 5): 6, (2, 6): 6} |
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597 for n in sorted(G.edges()): |
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598 assert almost_equal(b[n], b_answer[n]) |
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599 |
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600 def test_weighted_graph(self): |
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601 eList = [ |
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602 (0, 1, 5), |
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603 (0, 2, 4), |
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604 (0, 3, 3), |
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605 (0, 4, 2), |
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606 (1, 2, 4), |
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607 (1, 3, 1), |
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608 (1, 4, 3), |
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609 (2, 4, 5), |
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610 (3, 4, 4), |
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611 ] |
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612 G = nx.Graph() |
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613 G.add_weighted_edges_from(eList) |
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614 b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) |
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615 b_answer = { |
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616 (0, 1): 0.0, |
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617 (0, 2): 1.0, |
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618 (0, 3): 2.0, |
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619 (0, 4): 1.0, |
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620 (1, 2): 2.0, |
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621 (1, 3): 3.5, |
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622 (1, 4): 1.5, |
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623 (2, 4): 1.0, |
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624 (3, 4): 0.5, |
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625 } |
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626 for n in sorted(G.edges()): |
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627 assert almost_equal(b[n], b_answer[n]) |
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628 |
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629 def test_normalized_weighted_graph(self): |
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630 eList = [ |
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631 (0, 1, 5), |
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632 (0, 2, 4), |
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633 (0, 3, 3), |
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634 (0, 4, 2), |
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635 (1, 2, 4), |
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636 (1, 3, 1), |
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637 (1, 4, 3), |
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638 (2, 4, 5), |
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639 (3, 4, 4), |
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640 ] |
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641 G = nx.Graph() |
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642 G.add_weighted_edges_from(eList) |
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643 b = nx.edge_betweenness_centrality(G, weight="weight", normalized=True) |
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644 b_answer = { |
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645 (0, 1): 0.0, |
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646 (0, 2): 1.0, |
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647 (0, 3): 2.0, |
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648 (0, 4): 1.0, |
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649 (1, 2): 2.0, |
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650 (1, 3): 3.5, |
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651 (1, 4): 1.5, |
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652 (2, 4): 1.0, |
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653 (3, 4): 0.5, |
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654 } |
4f3585e2f14b
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
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655 norm = len(G) * (len(G) - 1) / 2 |
4f3585e2f14b
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
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656 for n in sorted(G.edges()): |
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"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
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657 assert almost_equal(b[n], b_answer[n] / norm) |