Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/centrality/current_flow_betweenness_subset.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 """Current-flow betweenness centrality measures for subsets of nodes.""" |
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2 import networkx as nx |
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3 from networkx.algorithms.centrality.flow_matrix import flow_matrix_row |
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4 from networkx.utils import not_implemented_for, reverse_cuthill_mckee_ordering |
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5 |
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6 __all__ = [ |
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7 "current_flow_betweenness_centrality_subset", |
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8 "edge_current_flow_betweenness_centrality_subset", |
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9 ] |
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10 |
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11 |
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12 @not_implemented_for("directed") |
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13 def current_flow_betweenness_centrality_subset( |
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14 G, sources, targets, normalized=True, weight=None, dtype=float, solver="lu" |
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15 ): |
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16 r"""Compute current-flow betweenness centrality for subsets of nodes. |
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17 |
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18 Current-flow betweenness centrality uses an electrical current |
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19 model for information spreading in contrast to betweenness |
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20 centrality which uses shortest paths. |
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21 |
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22 Current-flow betweenness centrality is also known as |
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23 random-walk betweenness centrality [2]_. |
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24 |
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25 Parameters |
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26 ---------- |
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27 G : graph |
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28 A NetworkX graph |
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29 |
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30 sources: list of nodes |
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31 Nodes to use as sources for current |
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32 |
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33 targets: list of nodes |
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34 Nodes to use as sinks for current |
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35 |
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36 normalized : bool, optional (default=True) |
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37 If True the betweenness values are normalized by b=b/(n-1)(n-2) where |
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38 n is the number of nodes in G. |
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39 |
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40 weight : string or None, optional (default=None) |
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41 Key for edge data used as the edge weight. |
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42 If None, then use 1 as each edge weight. |
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43 |
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44 dtype: data type (float) |
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45 Default data type for internal matrices. |
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46 Set to np.float32 for lower memory consumption. |
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47 |
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48 solver: string (default='lu') |
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49 Type of linear solver to use for computing the flow matrix. |
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50 Options are "full" (uses most memory), "lu" (recommended), and |
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51 "cg" (uses least memory). |
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52 |
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53 Returns |
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54 ------- |
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55 nodes : dictionary |
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56 Dictionary of nodes with betweenness centrality as the value. |
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57 |
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58 See Also |
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59 -------- |
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60 approximate_current_flow_betweenness_centrality |
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61 betweenness_centrality |
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62 edge_betweenness_centrality |
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63 edge_current_flow_betweenness_centrality |
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64 |
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65 Notes |
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66 ----- |
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67 Current-flow betweenness can be computed in $O(I(n-1)+mn \log n)$ |
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68 time [1]_, where $I(n-1)$ is the time needed to compute the |
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69 inverse Laplacian. For a full matrix this is $O(n^3)$ but using |
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70 sparse methods you can achieve $O(nm{\sqrt k})$ where $k$ is the |
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71 Laplacian matrix condition number. |
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72 |
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73 The space required is $O(nw)$ where $w$ is the width of the sparse |
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74 Laplacian matrix. Worse case is $w=n$ for $O(n^2)$. |
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75 |
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76 If the edges have a 'weight' attribute they will be used as |
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77 weights in this algorithm. Unspecified weights are set to 1. |
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78 |
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79 References |
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80 ---------- |
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81 .. [1] Centrality Measures Based on Current Flow. |
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82 Ulrik Brandes and Daniel Fleischer, |
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83 Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05). |
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84 LNCS 3404, pp. 533-544. Springer-Verlag, 2005. |
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85 http://algo.uni-konstanz.de/publications/bf-cmbcf-05.pdf |
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86 |
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87 .. [2] A measure of betweenness centrality based on random walks, |
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88 M. E. J. Newman, Social Networks 27, 39-54 (2005). |
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89 """ |
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90 from networkx.utils import reverse_cuthill_mckee_ordering |
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91 |
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92 try: |
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93 import numpy as np |
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94 except ImportError as e: |
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95 raise ImportError( |
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96 "current_flow_betweenness_centrality requires NumPy ", "http://numpy.org/" |
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97 ) from e |
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98 if not nx.is_connected(G): |
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99 raise nx.NetworkXError("Graph not connected.") |
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100 n = G.number_of_nodes() |
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101 ordering = list(reverse_cuthill_mckee_ordering(G)) |
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102 # make a copy with integer labels according to rcm ordering |
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103 # this could be done without a copy if we really wanted to |
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104 mapping = dict(zip(ordering, range(n))) |
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105 H = nx.relabel_nodes(G, mapping) |
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106 betweenness = dict.fromkeys(H, 0.0) # b[v]=0 for v in H |
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107 for row, (s, t) in flow_matrix_row(H, weight=weight, dtype=dtype, solver=solver): |
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108 for ss in sources: |
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109 i = mapping[ss] |
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110 for tt in targets: |
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111 j = mapping[tt] |
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112 betweenness[s] += 0.5 * np.abs(row[i] - row[j]) |
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113 betweenness[t] += 0.5 * np.abs(row[i] - row[j]) |
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114 if normalized: |
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115 nb = (n - 1.0) * (n - 2.0) # normalization factor |
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116 else: |
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117 nb = 2.0 |
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118 for v in H: |
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119 betweenness[v] = betweenness[v] / nb + 1.0 / (2 - n) |
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120 return {ordering[k]: v for k, v in betweenness.items()} |
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121 |
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122 |
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123 @not_implemented_for("directed") |
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124 def edge_current_flow_betweenness_centrality_subset( |
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125 G, sources, targets, normalized=True, weight=None, dtype=float, solver="lu" |
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126 ): |
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127 r"""Compute current-flow betweenness centrality for edges using subsets |
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128 of nodes. |
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129 |
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130 Current-flow betweenness centrality uses an electrical current |
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131 model for information spreading in contrast to betweenness |
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132 centrality which uses shortest paths. |
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133 |
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134 Current-flow betweenness centrality is also known as |
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135 random-walk betweenness centrality [2]_. |
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136 |
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137 Parameters |
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138 ---------- |
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139 G : graph |
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140 A NetworkX graph |
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141 |
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142 sources: list of nodes |
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143 Nodes to use as sources for current |
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144 |
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145 targets: list of nodes |
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146 Nodes to use as sinks for current |
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147 |
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148 normalized : bool, optional (default=True) |
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149 If True the betweenness values are normalized by b=b/(n-1)(n-2) where |
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150 n is the number of nodes in G. |
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151 |
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152 weight : string or None, optional (default=None) |
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153 Key for edge data used as the edge weight. |
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154 If None, then use 1 as each edge weight. |
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155 |
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156 dtype: data type (float) |
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157 Default data type for internal matrices. |
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158 Set to np.float32 for lower memory consumption. |
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159 |
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160 solver: string (default='lu') |
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161 Type of linear solver to use for computing the flow matrix. |
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162 Options are "full" (uses most memory), "lu" (recommended), and |
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163 "cg" (uses least memory). |
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164 |
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165 Returns |
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166 ------- |
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167 nodes : dict |
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168 Dictionary of edge tuples with betweenness centrality as the value. |
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169 |
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170 See Also |
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171 -------- |
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172 betweenness_centrality |
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173 edge_betweenness_centrality |
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174 current_flow_betweenness_centrality |
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175 |
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176 Notes |
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177 ----- |
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178 Current-flow betweenness can be computed in $O(I(n-1)+mn \log n)$ |
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179 time [1]_, where $I(n-1)$ is the time needed to compute the |
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180 inverse Laplacian. For a full matrix this is $O(n^3)$ but using |
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181 sparse methods you can achieve $O(nm{\sqrt k})$ where $k$ is the |
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182 Laplacian matrix condition number. |
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183 |
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184 The space required is $O(nw)$ where $w$ is the width of the sparse |
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185 Laplacian matrix. Worse case is $w=n$ for $O(n^2)$. |
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186 |
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187 If the edges have a 'weight' attribute they will be used as |
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188 weights in this algorithm. Unspecified weights are set to 1. |
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189 |
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190 References |
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191 ---------- |
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192 .. [1] Centrality Measures Based on Current Flow. |
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193 Ulrik Brandes and Daniel Fleischer, |
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194 Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05). |
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195 LNCS 3404, pp. 533-544. Springer-Verlag, 2005. |
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196 http://algo.uni-konstanz.de/publications/bf-cmbcf-05.pdf |
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197 |
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198 .. [2] A measure of betweenness centrality based on random walks, |
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199 M. E. J. Newman, Social Networks 27, 39-54 (2005). |
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200 """ |
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201 try: |
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202 import numpy as np |
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203 except ImportError as e: |
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204 raise ImportError( |
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205 "current_flow_betweenness_centrality requires NumPy " "http://numpy.org/" |
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206 ) from e |
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207 if not nx.is_connected(G): |
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208 raise nx.NetworkXError("Graph not connected.") |
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209 n = G.number_of_nodes() |
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210 ordering = list(reverse_cuthill_mckee_ordering(G)) |
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211 # make a copy with integer labels according to rcm ordering |
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212 # this could be done without a copy if we really wanted to |
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213 mapping = dict(zip(ordering, range(n))) |
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214 H = nx.relabel_nodes(G, mapping) |
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215 edges = (tuple(sorted((u, v))) for u, v in H.edges()) |
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216 betweenness = dict.fromkeys(edges, 0.0) |
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217 if normalized: |
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218 nb = (n - 1.0) * (n - 2.0) # normalization factor |
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219 else: |
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220 nb = 2.0 |
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221 for row, (e) in flow_matrix_row(H, weight=weight, dtype=dtype, solver=solver): |
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222 for ss in sources: |
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223 i = mapping[ss] |
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224 for tt in targets: |
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225 j = mapping[tt] |
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226 betweenness[e] += 0.5 * np.abs(row[i] - row[j]) |
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227 betweenness[e] /= nb |
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228 return {(ordering[s], ordering[t]): v for (s, t), v in betweenness.items()} |