Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/assortativity/correlation.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 """Node assortativity coefficients and correlation measures. |
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2 """ |
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3 from networkx.algorithms.assortativity.mixing import ( |
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4 degree_mixing_matrix, |
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5 attribute_mixing_matrix, |
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6 numeric_mixing_matrix, |
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7 ) |
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8 from networkx.algorithms.assortativity.pairs import node_degree_xy |
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9 |
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10 __all__ = [ |
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11 "degree_pearson_correlation_coefficient", |
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12 "degree_assortativity_coefficient", |
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13 "attribute_assortativity_coefficient", |
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14 "numeric_assortativity_coefficient", |
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15 ] |
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16 |
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17 |
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18 def degree_assortativity_coefficient(G, x="out", y="in", weight=None, nodes=None): |
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19 """Compute degree assortativity of graph. |
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20 |
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21 Assortativity measures the similarity of connections |
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22 in the graph with respect to the node degree. |
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23 |
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24 Parameters |
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25 ---------- |
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26 G : NetworkX graph |
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27 |
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28 x: string ('in','out') |
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29 The degree type for source node (directed graphs only). |
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30 |
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31 y: string ('in','out') |
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32 The degree type for target node (directed graphs only). |
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33 |
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34 weight: string or None, optional (default=None) |
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35 The edge attribute that holds the numerical value used |
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36 as a weight. If None, then each edge has weight 1. |
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37 The degree is the sum of the edge weights adjacent to the node. |
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38 |
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39 nodes: list or iterable (optional) |
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40 Compute degree assortativity only for nodes in container. |
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41 The default is all nodes. |
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42 |
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43 Returns |
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44 ------- |
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45 r : float |
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46 Assortativity of graph by degree. |
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47 |
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48 Examples |
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49 -------- |
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50 >>> G = nx.path_graph(4) |
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51 >>> r = nx.degree_assortativity_coefficient(G) |
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52 >>> print(f"{r:3.1f}") |
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53 -0.5 |
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54 |
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55 See Also |
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56 -------- |
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57 attribute_assortativity_coefficient |
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58 numeric_assortativity_coefficient |
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59 neighbor_connectivity |
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60 degree_mixing_dict |
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61 degree_mixing_matrix |
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62 |
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63 Notes |
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64 ----- |
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65 This computes Eq. (21) in Ref. [1]_ , where e is the joint |
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66 probability distribution (mixing matrix) of the degrees. If G is |
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67 directed than the matrix e is the joint probability of the |
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68 user-specified degree type for the source and target. |
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69 |
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70 References |
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71 ---------- |
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72 .. [1] M. E. J. Newman, Mixing patterns in networks, |
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73 Physical Review E, 67 026126, 2003 |
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74 .. [2] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M. |
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75 Edge direction and the structure of networks, PNAS 107, 10815-20 (2010). |
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76 """ |
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77 M = degree_mixing_matrix(G, x=x, y=y, nodes=nodes, weight=weight) |
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78 return numeric_ac(M) |
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79 |
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80 |
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81 def degree_pearson_correlation_coefficient(G, x="out", y="in", weight=None, nodes=None): |
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82 """Compute degree assortativity of graph. |
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83 |
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84 Assortativity measures the similarity of connections |
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85 in the graph with respect to the node degree. |
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86 |
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87 This is the same as degree_assortativity_coefficient but uses the |
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88 potentially faster scipy.stats.pearsonr function. |
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89 |
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90 Parameters |
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91 ---------- |
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92 G : NetworkX graph |
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93 |
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94 x: string ('in','out') |
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95 The degree type for source node (directed graphs only). |
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96 |
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97 y: string ('in','out') |
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98 The degree type for target node (directed graphs only). |
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99 |
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100 weight: string or None, optional (default=None) |
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101 The edge attribute that holds the numerical value used |
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102 as a weight. If None, then each edge has weight 1. |
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103 The degree is the sum of the edge weights adjacent to the node. |
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104 |
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105 nodes: list or iterable (optional) |
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106 Compute pearson correlation of degrees only for specified nodes. |
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107 The default is all nodes. |
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108 |
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109 Returns |
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110 ------- |
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111 r : float |
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112 Assortativity of graph by degree. |
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113 |
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114 Examples |
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115 -------- |
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116 >>> G = nx.path_graph(4) |
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117 >>> r = nx.degree_pearson_correlation_coefficient(G) |
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118 >>> print(f"{r:3.1f}") |
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119 -0.5 |
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120 |
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121 Notes |
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122 ----- |
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123 This calls scipy.stats.pearsonr. |
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124 |
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125 References |
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126 ---------- |
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127 .. [1] M. E. J. Newman, Mixing patterns in networks |
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128 Physical Review E, 67 026126, 2003 |
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129 .. [2] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M. |
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130 Edge direction and the structure of networks, PNAS 107, 10815-20 (2010). |
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131 """ |
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132 try: |
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133 import scipy.stats as stats |
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134 except ImportError as e: |
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135 raise ImportError("Assortativity requires SciPy:" "http://scipy.org/ ") from e |
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136 xy = node_degree_xy(G, x=x, y=y, nodes=nodes, weight=weight) |
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137 x, y = zip(*xy) |
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138 return stats.pearsonr(x, y)[0] |
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139 |
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140 |
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141 def attribute_assortativity_coefficient(G, attribute, nodes=None): |
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142 """Compute assortativity for node attributes. |
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143 |
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144 Assortativity measures the similarity of connections |
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145 in the graph with respect to the given attribute. |
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146 |
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147 Parameters |
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148 ---------- |
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149 G : NetworkX graph |
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150 |
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151 attribute : string |
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152 Node attribute key |
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153 |
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154 nodes: list or iterable (optional) |
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155 Compute attribute assortativity for nodes in container. |
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156 The default is all nodes. |
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157 |
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158 Returns |
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159 ------- |
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160 r: float |
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161 Assortativity of graph for given attribute |
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162 |
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163 Examples |
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164 -------- |
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165 >>> G = nx.Graph() |
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166 >>> G.add_nodes_from([0, 1], color="red") |
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167 >>> G.add_nodes_from([2, 3], color="blue") |
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168 >>> G.add_edges_from([(0, 1), (2, 3)]) |
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169 >>> print(nx.attribute_assortativity_coefficient(G, "color")) |
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170 1.0 |
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171 |
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172 Notes |
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173 ----- |
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174 This computes Eq. (2) in Ref. [1]_ , (trace(M)-sum(M^2))/(1-sum(M^2)), |
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175 where M is the joint probability distribution (mixing matrix) |
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176 of the specified attribute. |
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177 |
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178 References |
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179 ---------- |
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180 .. [1] M. E. J. Newman, Mixing patterns in networks, |
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181 Physical Review E, 67 026126, 2003 |
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182 """ |
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183 M = attribute_mixing_matrix(G, attribute, nodes) |
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184 return attribute_ac(M) |
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185 |
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186 |
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187 def numeric_assortativity_coefficient(G, attribute, nodes=None): |
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188 """Compute assortativity for numerical node attributes. |
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189 |
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190 Assortativity measures the similarity of connections |
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191 in the graph with respect to the given numeric attribute. |
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192 The numeric attribute must be an integer. |
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193 |
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194 Parameters |
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195 ---------- |
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196 G : NetworkX graph |
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197 |
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198 attribute : string |
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199 Node attribute key. The corresponding attribute value must be an |
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200 integer. |
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201 |
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202 nodes: list or iterable (optional) |
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203 Compute numeric assortativity only for attributes of nodes in |
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204 container. The default is all nodes. |
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205 |
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206 Returns |
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207 ------- |
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208 r: float |
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209 Assortativity of graph for given attribute |
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210 |
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211 Examples |
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212 -------- |
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213 >>> G = nx.Graph() |
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214 >>> G.add_nodes_from([0, 1], size=2) |
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215 >>> G.add_nodes_from([2, 3], size=3) |
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216 >>> G.add_edges_from([(0, 1), (2, 3)]) |
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217 >>> print(nx.numeric_assortativity_coefficient(G, "size")) |
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218 1.0 |
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219 |
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220 Notes |
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221 ----- |
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222 This computes Eq. (21) in Ref. [1]_ , for the mixing matrix of |
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223 of the specified attribute. |
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224 |
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225 References |
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226 ---------- |
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227 .. [1] M. E. J. Newman, Mixing patterns in networks |
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228 Physical Review E, 67 026126, 2003 |
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229 """ |
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230 a = numeric_mixing_matrix(G, attribute, nodes) |
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231 return numeric_ac(a) |
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232 |
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233 |
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234 def attribute_ac(M): |
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235 """Compute assortativity for attribute matrix M. |
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236 |
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237 Parameters |
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238 ---------- |
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239 M : numpy.ndarray |
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240 2D ndarray representing the attribute mixing matrix. |
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241 |
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242 Notes |
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243 ----- |
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244 This computes Eq. (2) in Ref. [1]_ , (trace(e)-sum(e^2))/(1-sum(e^2)), |
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245 where e is the joint probability distribution (mixing matrix) |
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246 of the specified attribute. |
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247 |
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248 References |
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249 ---------- |
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250 .. [1] M. E. J. Newman, Mixing patterns in networks, |
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251 Physical Review E, 67 026126, 2003 |
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252 """ |
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253 try: |
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254 import numpy |
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255 except ImportError as e: |
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256 raise ImportError( |
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257 "attribute_assortativity requires " "NumPy: http://scipy.org/" |
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258 ) from e |
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259 if M.sum() != 1.0: |
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260 M = M / M.sum() |
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261 s = (M @ M).sum() |
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262 t = M.trace() |
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263 r = (t - s) / (1 - s) |
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264 return r |
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265 |
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266 |
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267 def numeric_ac(M): |
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268 # M is a numpy matrix or array |
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269 # numeric assortativity coefficient, pearsonr |
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270 try: |
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271 import numpy |
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272 except ImportError as e: |
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273 raise ImportError( |
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274 "numeric_assortativity requires " "NumPy: http://scipy.org/" |
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275 ) from e |
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276 if M.sum() != 1.0: |
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277 M = M / float(M.sum()) |
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278 nx, ny = M.shape # nx=ny |
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279 x = numpy.arange(nx) |
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280 y = numpy.arange(ny) |
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281 a = M.sum(axis=0) |
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282 b = M.sum(axis=1) |
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283 vara = (a * x ** 2).sum() - ((a * x).sum()) ** 2 |
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284 varb = (b * x ** 2).sum() - ((b * x).sum()) ** 2 |
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285 xy = numpy.outer(x, y) |
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286 ab = numpy.outer(a, b) |
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287 return (xy * (M - ab)).sum() / numpy.sqrt(vara * varb) |