Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/community/asyn_fluid.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 """Asynchronous Fluid Communities algorithm for community detection.""" |
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2 |
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3 from collections import Counter |
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4 from networkx.exception import NetworkXError |
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5 from networkx.algorithms.components import is_connected |
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6 from networkx.utils import groups |
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7 from networkx.utils import not_implemented_for |
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8 from networkx.utils import py_random_state |
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9 |
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10 __all__ = ["asyn_fluidc"] |
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11 |
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12 |
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13 @py_random_state(3) |
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14 @not_implemented_for("directed", "multigraph") |
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15 def asyn_fluidc(G, k, max_iter=100, seed=None): |
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16 """Returns communities in `G` as detected by Fluid Communities algorithm. |
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17 |
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18 The asynchronous fluid communities algorithm is described in |
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19 [1]_. The algorithm is based on the simple idea of fluids interacting |
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20 in an environment, expanding and pushing each other. Its initialization is |
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21 random, so found communities may vary on different executions. |
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22 |
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23 The algorithm proceeds as follows. First each of the initial k communities |
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24 is initialized in a random vertex in the graph. Then the algorithm iterates |
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25 over all vertices in a random order, updating the community of each vertex |
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26 based on its own community and the communities of its neighbours. This |
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27 process is performed several times until convergence. |
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28 At all times, each community has a total density of 1, which is equally |
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29 distributed among the vertices it contains. If a vertex changes of |
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30 community, vertex densities of affected communities are adjusted |
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31 immediately. When a complete iteration over all vertices is done, such that |
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32 no vertex changes the community it belongs to, the algorithm has converged |
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33 and returns. |
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34 |
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35 This is the original version of the algorithm described in [1]_. |
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36 Unfortunately, it does not support weighted graphs yet. |
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37 |
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38 Parameters |
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39 ---------- |
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40 G : Graph |
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41 |
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42 k : integer |
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43 The number of communities to be found. |
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44 |
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45 max_iter : integer |
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46 The number of maximum iterations allowed. By default 100. |
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47 |
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48 seed : integer, random_state, or None (default) |
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49 Indicator of random number generation state. |
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50 See :ref:`Randomness<randomness>`. |
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51 |
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52 Returns |
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53 ------- |
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54 communities : iterable |
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55 Iterable of communities given as sets of nodes. |
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56 |
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57 Notes |
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58 ----- |
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59 k variable is not an optional argument. |
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60 |
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61 References |
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62 ---------- |
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63 .. [1] Parés F., Garcia-Gasulla D. et al. "Fluid Communities: A |
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64 Competitive and Highly Scalable Community Detection Algorithm". |
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65 [https://arxiv.org/pdf/1703.09307.pdf]. |
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66 """ |
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67 # Initial checks |
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68 if not isinstance(k, int): |
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69 raise NetworkXError("k must be an integer.") |
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70 if not k > 0: |
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71 raise NetworkXError("k must be greater than 0.") |
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72 if not is_connected(G): |
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73 raise NetworkXError("Fluid Communities require connected Graphs.") |
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74 if len(G) < k: |
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75 raise NetworkXError("k cannot be bigger than the number of nodes.") |
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76 # Initialization |
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77 max_density = 1.0 |
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78 vertices = list(G) |
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79 seed.shuffle(vertices) |
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80 communities = {n: i for i, n in enumerate(vertices[:k])} |
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81 density = {} |
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82 com_to_numvertices = {} |
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83 for vertex in communities.keys(): |
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84 com_to_numvertices[communities[vertex]] = 1 |
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85 density[communities[vertex]] = max_density |
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86 # Set up control variables and start iterating |
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87 iter_count = 0 |
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88 cont = True |
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89 while cont: |
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90 cont = False |
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91 iter_count += 1 |
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92 # Loop over all vertices in graph in a random order |
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93 vertices = list(G) |
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94 seed.shuffle(vertices) |
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95 for vertex in vertices: |
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96 # Updating rule |
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97 com_counter = Counter() |
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98 # Take into account self vertex community |
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99 try: |
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100 com_counter.update({communities[vertex]: density[communities[vertex]]}) |
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101 except KeyError: |
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102 pass |
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103 # Gather neighbour vertex communities |
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104 for v in G[vertex]: |
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105 try: |
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106 com_counter.update({communities[v]: density[communities[v]]}) |
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107 except KeyError: |
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108 continue |
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109 # Check which is the community with highest density |
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110 new_com = -1 |
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111 if len(com_counter.keys()) > 0: |
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112 max_freq = max(com_counter.values()) |
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113 best_communities = [ |
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114 com |
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115 for com, freq in com_counter.items() |
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116 if (max_freq - freq) < 0.0001 |
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117 ] |
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118 # If actual vertex com in best communities, it is preserved |
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119 try: |
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120 if communities[vertex] in best_communities: |
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121 new_com = communities[vertex] |
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122 except KeyError: |
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123 pass |
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124 # If vertex community changes... |
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125 if new_com == -1: |
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126 # Set flag of non-convergence |
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127 cont = True |
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128 # Randomly chose a new community from candidates |
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129 new_com = seed.choice(best_communities) |
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130 # Update previous community status |
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131 try: |
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132 com_to_numvertices[communities[vertex]] -= 1 |
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133 density[communities[vertex]] = ( |
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134 max_density / com_to_numvertices[communities[vertex]] |
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135 ) |
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136 except KeyError: |
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137 pass |
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138 # Update new community status |
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139 communities[vertex] = new_com |
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140 com_to_numvertices[communities[vertex]] += 1 |
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141 density[communities[vertex]] = ( |
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142 max_density / com_to_numvertices[communities[vertex]] |
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143 ) |
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144 # If maximum iterations reached --> output actual results |
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145 if iter_count > max_iter: |
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146 break |
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147 # Return results by grouping communities as list of vertices |
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148 return iter(groups(communities).values()) |