Mercurial > repos > guerler > springsuite
annotate planemo/lib/python3.7/site-packages/networkx/algorithms/clique.py @ 1:56ad4e20f292 draft
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author | guerler |
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date | Fri, 31 Jul 2020 00:32:28 -0400 |
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1 # Copyright (C) 2004-2019 by |
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2 # Aric Hagberg <hagberg@lanl.gov> |
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3 # Dan Schult <dschult@colgate.edu> |
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4 # Pieter Swart <swart@lanl.gov> |
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5 # All rights reserved. |
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6 # BSD license. |
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7 """Functions for finding and manipulating cliques. |
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8 |
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9 Finding the largest clique in a graph is NP-complete problem, so most of |
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10 these algorithms have an exponential running time; for more information, |
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11 see the Wikipedia article on the clique problem [1]_. |
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12 |
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13 .. [1] clique problem:: https://en.wikipedia.org/wiki/Clique_problem |
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14 |
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15 """ |
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16 from collections import deque |
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17 from itertools import chain |
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18 from itertools import combinations |
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19 from itertools import islice |
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20 try: |
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21 from itertools import ifilter as filter |
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22 except ImportError: |
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23 pass |
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24 import networkx as nx |
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25 from networkx.utils import not_implemented_for |
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26 __author__ = """Dan Schult (dschult@colgate.edu)""" |
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27 __all__ = ['find_cliques', 'find_cliques_recursive', 'make_max_clique_graph', |
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28 'make_clique_bipartite', 'graph_clique_number', |
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29 'graph_number_of_cliques', 'node_clique_number', |
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30 'number_of_cliques', 'cliques_containing_node', |
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31 'enumerate_all_cliques'] |
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32 |
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33 |
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34 @not_implemented_for('directed') |
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35 def enumerate_all_cliques(G): |
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36 """Returns all cliques in an undirected graph. |
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37 |
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38 This function returns an iterator over cliques, each of which is a |
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39 list of nodes. The iteration is ordered by cardinality of the |
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40 cliques: first all cliques of size one, then all cliques of size |
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41 two, etc. |
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42 |
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43 Parameters |
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44 ---------- |
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45 G : NetworkX graph |
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46 An undirected graph. |
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47 |
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48 Returns |
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49 ------- |
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50 iterator |
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51 An iterator over cliques, each of which is a list of nodes in |
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52 `G`. The cliques are ordered according to size. |
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53 |
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54 Notes |
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55 ----- |
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56 To obtain a list of all cliques, use |
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57 `list(enumerate_all_cliques(G))`. However, be aware that in the |
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58 worst-case, the length of this list can be exponential in the number |
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59 of nodes in the graph (for example, when the graph is the complete |
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60 graph). This function avoids storing all cliques in memory by only |
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61 keeping current candidate node lists in memory during its search. |
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62 |
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63 The implementation is adapted from the algorithm by Zhang, et |
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64 al. (2005) [1]_ to output all cliques discovered. |
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65 |
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66 This algorithm ignores self-loops and parallel edges, since cliques |
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67 are not conventionally defined with such edges. |
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68 |
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69 References |
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70 ---------- |
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71 .. [1] Yun Zhang, Abu-Khzam, F.N., Baldwin, N.E., Chesler, E.J., |
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72 Langston, M.A., Samatova, N.F., |
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73 "Genome-Scale Computational Approaches to Memory-Intensive |
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74 Applications in Systems Biology". |
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75 *Supercomputing*, 2005. Proceedings of the ACM/IEEE SC 2005 |
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76 Conference, pp. 12, 12--18 Nov. 2005. |
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77 <https://doi.org/10.1109/SC.2005.29>. |
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78 |
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79 """ |
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80 index = {} |
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81 nbrs = {} |
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82 for u in G: |
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83 index[u] = len(index) |
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84 # Neighbors of u that appear after u in the iteration order of G. |
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85 nbrs[u] = {v for v in G[u] if v not in index} |
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86 |
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87 queue = deque(([u], sorted(nbrs[u], key=index.__getitem__)) for u in G) |
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88 # Loop invariants: |
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89 # 1. len(base) is nondecreasing. |
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90 # 2. (base + cnbrs) is sorted with respect to the iteration order of G. |
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91 # 3. cnbrs is a set of common neighbors of nodes in base. |
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92 while queue: |
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93 base, cnbrs = map(list, queue.popleft()) |
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94 yield base |
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95 for i, u in enumerate(cnbrs): |
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96 # Use generators to reduce memory consumption. |
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97 queue.append((chain(base, [u]), |
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98 filter(nbrs[u].__contains__, |
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99 islice(cnbrs, i + 1, None)))) |
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100 |
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101 |
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102 @not_implemented_for('directed') |
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103 def find_cliques(G): |
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104 """Returns all maximal cliques in an undirected graph. |
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105 |
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106 For each node *v*, a *maximal clique for v* is a largest complete |
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107 subgraph containing *v*. The largest maximal clique is sometimes |
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108 called the *maximum clique*. |
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109 |
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110 This function returns an iterator over cliques, each of which is a |
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111 list of nodes. It is an iterative implementation, so should not |
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112 suffer from recursion depth issues. |
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113 |
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114 Parameters |
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115 ---------- |
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116 G : NetworkX graph |
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117 An undirected graph. |
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118 |
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119 Returns |
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120 ------- |
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121 iterator |
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122 An iterator over maximal cliques, each of which is a list of |
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123 nodes in `G`. The order of cliques is arbitrary. |
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124 |
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125 See Also |
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126 -------- |
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127 find_cliques_recursive |
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128 A recursive version of the same algorithm. |
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129 |
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130 Notes |
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131 ----- |
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132 To obtain a list of all maximal cliques, use |
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133 `list(find_cliques(G))`. However, be aware that in the worst-case, |
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134 the length of this list can be exponential in the number of nodes in |
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135 the graph. This function avoids storing all cliques in memory by |
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136 only keeping current candidate node lists in memory during its search. |
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137 |
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138 This implementation is based on the algorithm published by Bron and |
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139 Kerbosch (1973) [1]_, as adapted by Tomita, Tanaka and Takahashi |
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140 (2006) [2]_ and discussed in Cazals and Karande (2008) [3]_. It |
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141 essentially unrolls the recursion used in the references to avoid |
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142 issues of recursion stack depth (for a recursive implementation, see |
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143 :func:`find_cliques_recursive`). |
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144 |
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145 This algorithm ignores self-loops and parallel edges, since cliques |
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146 are not conventionally defined with such edges. |
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147 |
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148 References |
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149 ---------- |
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150 .. [1] Bron, C. and Kerbosch, J. |
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151 "Algorithm 457: finding all cliques of an undirected graph". |
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152 *Communications of the ACM* 16, 9 (Sep. 1973), 575--577. |
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153 <http://portal.acm.org/citation.cfm?doid=362342.362367> |
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154 |
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155 .. [2] Etsuji Tomita, Akira Tanaka, Haruhisa Takahashi, |
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156 "The worst-case time complexity for generating all maximal |
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157 cliques and computational experiments", |
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158 *Theoretical Computer Science*, Volume 363, Issue 1, |
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159 Computing and Combinatorics, |
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160 10th Annual International Conference on |
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161 Computing and Combinatorics (COCOON 2004), 25 October 2006, Pages 28--42 |
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162 <https://doi.org/10.1016/j.tcs.2006.06.015> |
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163 |
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164 .. [3] F. Cazals, C. Karande, |
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165 "A note on the problem of reporting maximal cliques", |
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166 *Theoretical Computer Science*, |
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167 Volume 407, Issues 1--3, 6 November 2008, Pages 564--568, |
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168 <https://doi.org/10.1016/j.tcs.2008.05.010> |
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169 |
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170 """ |
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171 if len(G) == 0: |
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172 return |
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173 |
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174 adj = {u: {v for v in G[u] if v != u} for u in G} |
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175 Q = [None] |
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176 |
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177 subg = set(G) |
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178 cand = set(G) |
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179 u = max(subg, key=lambda u: len(cand & adj[u])) |
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180 ext_u = cand - adj[u] |
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181 stack = [] |
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182 |
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183 try: |
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184 while True: |
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185 if ext_u: |
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186 q = ext_u.pop() |
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187 cand.remove(q) |
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188 Q[-1] = q |
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189 adj_q = adj[q] |
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190 subg_q = subg & adj_q |
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191 if not subg_q: |
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192 yield Q[:] |
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193 else: |
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194 cand_q = cand & adj_q |
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195 if cand_q: |
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196 stack.append((subg, cand, ext_u)) |
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197 Q.append(None) |
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198 subg = subg_q |
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199 cand = cand_q |
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200 u = max(subg, key=lambda u: len(cand & adj[u])) |
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201 ext_u = cand - adj[u] |
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202 else: |
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203 Q.pop() |
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204 subg, cand, ext_u = stack.pop() |
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205 except IndexError: |
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206 pass |
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207 |
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208 |
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209 # TODO Should this also be not implemented for directed graphs? |
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210 def find_cliques_recursive(G): |
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211 """Returns all maximal cliques in a graph. |
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212 |
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213 For each node *v*, a *maximal clique for v* is a largest complete |
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214 subgraph containing *v*. The largest maximal clique is sometimes |
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215 called the *maximum clique*. |
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216 |
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217 This function returns an iterator over cliques, each of which is a |
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218 list of nodes. It is a recursive implementation, so may suffer from |
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219 recursion depth issues. |
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220 |
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221 Parameters |
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222 ---------- |
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223 G : NetworkX graph |
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224 |
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225 Returns |
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226 ------- |
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227 iterator |
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228 An iterator over maximal cliques, each of which is a list of |
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229 nodes in `G`. The order of cliques is arbitrary. |
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230 |
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231 See Also |
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232 -------- |
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233 find_cliques |
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234 An iterative version of the same algorithm. |
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235 |
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236 Notes |
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237 ----- |
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238 To obtain a list of all maximal cliques, use |
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239 `list(find_cliques_recursive(G))`. However, be aware that in the |
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240 worst-case, the length of this list can be exponential in the number |
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241 of nodes in the graph. This function avoids storing all cliques in memory |
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242 by only keeping current candidate node lists in memory during its search. |
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243 |
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244 This implementation is based on the algorithm published by Bron and |
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245 Kerbosch (1973) [1]_, as adapted by Tomita, Tanaka and Takahashi |
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246 (2006) [2]_ and discussed in Cazals and Karande (2008) [3]_. For a |
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247 non-recursive implementation, see :func:`find_cliques`. |
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248 |
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249 This algorithm ignores self-loops and parallel edges, since cliques |
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250 are not conventionally defined with such edges. |
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251 |
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252 References |
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253 ---------- |
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254 .. [1] Bron, C. and Kerbosch, J. |
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255 "Algorithm 457: finding all cliques of an undirected graph". |
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256 *Communications of the ACM* 16, 9 (Sep. 1973), 575--577. |
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257 <http://portal.acm.org/citation.cfm?doid=362342.362367> |
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258 |
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259 .. [2] Etsuji Tomita, Akira Tanaka, Haruhisa Takahashi, |
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260 "The worst-case time complexity for generating all maximal |
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261 cliques and computational experiments", |
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262 *Theoretical Computer Science*, Volume 363, Issue 1, |
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263 Computing and Combinatorics, |
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264 10th Annual International Conference on |
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265 Computing and Combinatorics (COCOON 2004), 25 October 2006, Pages 28--42 |
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266 <https://doi.org/10.1016/j.tcs.2006.06.015> |
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267 |
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268 .. [3] F. Cazals, C. Karande, |
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269 "A note on the problem of reporting maximal cliques", |
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270 *Theoretical Computer Science*, |
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271 Volume 407, Issues 1--3, 6 November 2008, Pages 564--568, |
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272 <https://doi.org/10.1016/j.tcs.2008.05.010> |
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273 |
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274 """ |
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275 if len(G) == 0: |
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276 return iter([]) |
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277 |
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278 adj = {u: {v for v in G[u] if v != u} for u in G} |
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279 Q = [] |
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280 |
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281 def expand(subg, cand): |
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282 u = max(subg, key=lambda u: len(cand & adj[u])) |
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283 for q in cand - adj[u]: |
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284 cand.remove(q) |
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285 Q.append(q) |
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286 adj_q = adj[q] |
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287 subg_q = subg & adj_q |
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288 if not subg_q: |
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289 yield Q[:] |
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290 else: |
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291 cand_q = cand & adj_q |
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292 if cand_q: |
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293 for clique in expand(subg_q, cand_q): |
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294 yield clique |
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295 Q.pop() |
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296 |
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297 return expand(set(G), set(G)) |
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298 |
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299 |
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300 def make_max_clique_graph(G, create_using=None): |
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301 """Returns the maximal clique graph of the given graph. |
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302 |
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303 The nodes of the maximal clique graph of `G` are the cliques of |
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304 `G` and an edge joins two cliques if the cliques are not disjoint. |
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305 |
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306 Parameters |
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307 ---------- |
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308 G : NetworkX graph |
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309 |
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310 create_using : NetworkX graph constructor, optional (default=nx.Graph) |
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311 Graph type to create. If graph instance, then cleared before populated. |
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312 |
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313 Returns |
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314 ------- |
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315 NetworkX graph |
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316 A graph whose nodes are the cliques of `G` and whose edges |
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317 join two cliques if they are not disjoint. |
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318 |
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319 Notes |
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320 ----- |
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321 This function behaves like the following code:: |
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322 |
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323 import networkx as nx |
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324 G = nx.make_clique_bipartite(G) |
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325 cliques = [v for v in G.nodes() if G.nodes[v]['bipartite'] == 0] |
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326 G = nx.bipartite.project(G, cliques) |
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327 G = nx.relabel_nodes(G, {-v: v - 1 for v in G}) |
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328 |
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329 It should be faster, though, since it skips all the intermediate |
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330 steps. |
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331 |
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332 """ |
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333 if create_using is None: |
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334 B = G.__class__() |
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335 else: |
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336 B = nx.empty_graph(0, create_using) |
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337 cliques = list(enumerate(set(c) for c in find_cliques(G))) |
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338 # Add a numbered node for each clique. |
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339 B.add_nodes_from(i for i, c in cliques) |
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340 # Join cliques by an edge if they share a node. |
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341 clique_pairs = combinations(cliques, 2) |
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342 B.add_edges_from((i, j) for (i, c1), (j, c2) in clique_pairs if c1 & c2) |
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343 return B |
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344 |
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345 |
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346 def make_clique_bipartite(G, fpos=None, create_using=None, name=None): |
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347 """Returns the bipartite clique graph corresponding to `G`. |
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348 |
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349 In the returned bipartite graph, the "bottom" nodes are the nodes of |
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350 `G` and the "top" nodes represent the maximal cliques of `G`. |
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351 There is an edge from node *v* to clique *C* in the returned graph |
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352 if and only if *v* is an element of *C*. |
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353 |
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354 Parameters |
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355 ---------- |
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356 G : NetworkX graph |
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357 An undirected graph. |
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358 |
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359 fpos : bool |
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360 If True or not None, the returned graph will have an |
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361 additional attribute, `pos`, a dictionary mapping node to |
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362 position in the Euclidean plane. |
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363 |
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364 create_using : NetworkX graph constructor, optional (default=nx.Graph) |
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365 Graph type to create. If graph instance, then cleared before populated. |
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366 |
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367 Returns |
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368 ------- |
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369 NetworkX graph |
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370 A bipartite graph whose "bottom" set is the nodes of the graph |
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371 `G`, whose "top" set is the cliques of `G`, and whose edges |
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372 join nodes of `G` to the cliques that contain them. |
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373 |
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374 The nodes of the graph `G` have the node attribute |
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375 'bipartite' set to 1 and the nodes representing cliques |
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376 have the node attribute 'bipartite' set to 0, as is the |
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377 convention for bipartite graphs in NetworkX. |
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378 |
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379 """ |
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380 B = nx.empty_graph(0, create_using) |
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381 B.clear() |
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382 # The "bottom" nodes in the bipartite graph are the nodes of the |
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383 # original graph, G. |
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384 B.add_nodes_from(G, bipartite=1) |
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385 for i, cl in enumerate(find_cliques(G)): |
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386 # The "top" nodes in the bipartite graph are the cliques. These |
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387 # nodes get negative numbers as labels. |
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388 name = -i - 1 |
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389 B.add_node(name, bipartite=0) |
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390 B.add_edges_from((v, name) for v in cl) |
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391 return B |
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392 |
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393 |
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394 def graph_clique_number(G, cliques=None): |
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395 """Returns the clique number of the graph. |
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396 |
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397 The *clique number* of a graph is the size of the largest clique in |
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398 the graph. |
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399 |
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400 Parameters |
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401 ---------- |
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402 G : NetworkX graph |
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403 An undirected graph. |
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404 |
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405 cliques : list |
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406 A list of cliques, each of which is itself a list of nodes. If |
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407 not specified, the list of all cliques will be computed, as by |
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408 :func:`find_cliques`. |
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409 |
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410 Returns |
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411 ------- |
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412 int |
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413 The size of the largest clique in `G`. |
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414 |
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415 Notes |
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416 ----- |
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417 You should provide `cliques` if you have already computed the list |
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418 of maximal cliques, in order to avoid an exponential time search for |
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419 maximal cliques. |
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420 |
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421 """ |
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422 if cliques is None: |
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423 cliques = find_cliques(G) |
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424 if len(G.nodes) < 1: |
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425 return 0 |
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426 return max([len(c) for c in cliques] or [1]) |
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427 |
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428 |
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429 def graph_number_of_cliques(G, cliques=None): |
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430 """Returns the number of maximal cliques in the graph. |
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431 |
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432 Parameters |
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433 ---------- |
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434 G : NetworkX graph |
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435 An undirected graph. |
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436 |
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437 cliques : list |
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438 A list of cliques, each of which is itself a list of nodes. If |
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439 not specified, the list of all cliques will be computed, as by |
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440 :func:`find_cliques`. |
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441 |
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442 Returns |
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443 ------- |
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444 int |
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445 The number of maximal cliques in `G`. |
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446 |
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447 Notes |
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448 ----- |
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449 You should provide `cliques` if you have already computed the list |
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450 of maximal cliques, in order to avoid an exponential time search for |
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451 maximal cliques. |
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452 |
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453 """ |
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454 if cliques is None: |
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455 cliques = list(find_cliques(G)) |
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456 return len(cliques) |
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457 |
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458 |
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459 def node_clique_number(G, nodes=None, cliques=None): |
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460 """ Returns the size of the largest maximal clique containing |
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461 each given node. |
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462 |
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463 Returns a single or list depending on input nodes. |
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464 Optional list of cliques can be input if already computed. |
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465 """ |
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466 if cliques is None: |
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467 if nodes is not None: |
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468 # Use ego_graph to decrease size of graph |
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469 if isinstance(nodes, list): |
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470 d = {} |
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471 for n in nodes: |
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472 H = nx.ego_graph(G, n) |
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473 d[n] = max((len(c) for c in find_cliques(H))) |
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474 else: |
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475 H = nx.ego_graph(G, nodes) |
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476 d = max((len(c) for c in find_cliques(H))) |
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477 return d |
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478 # nodes is None--find all cliques |
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479 cliques = list(find_cliques(G)) |
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480 |
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481 if nodes is None: |
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482 nodes = list(G.nodes()) # none, get entire graph |
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483 |
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484 if not isinstance(nodes, list): # check for a list |
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485 v = nodes |
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486 # assume it is a single value |
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487 d = max([len(c) for c in cliques if v in c]) |
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488 else: |
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489 d = {} |
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490 for v in nodes: |
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491 d[v] = max([len(c) for c in cliques if v in c]) |
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492 return d |
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493 |
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494 # if nodes is None: # none, use entire graph |
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495 # nodes=G.nodes() |
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496 # elif not isinstance(nodes, list): # check for a list |
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497 # nodes=[nodes] # assume it is a single value |
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498 |
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499 # if cliques is None: |
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500 # cliques=list(find_cliques(G)) |
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501 # d={} |
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502 # for v in nodes: |
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503 # d[v]=max([len(c) for c in cliques if v in c]) |
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504 |
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505 # if nodes in G: |
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506 # return d[v] #return single value |
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507 # return d |
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508 |
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509 |
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510 def number_of_cliques(G, nodes=None, cliques=None): |
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511 """Returns the number of maximal cliques for each node. |
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512 |
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513 Returns a single or list depending on input nodes. |
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514 Optional list of cliques can be input if already computed. |
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515 """ |
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516 if cliques is None: |
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517 cliques = list(find_cliques(G)) |
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518 |
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519 if nodes is None: |
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520 nodes = list(G.nodes()) # none, get entire graph |
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521 |
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522 if not isinstance(nodes, list): # check for a list |
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523 v = nodes |
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524 # assume it is a single value |
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525 numcliq = len([1 for c in cliques if v in c]) |
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526 else: |
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527 numcliq = {} |
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528 for v in nodes: |
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529 numcliq[v] = len([1 for c in cliques if v in c]) |
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530 return numcliq |
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531 |
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532 |
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533 def cliques_containing_node(G, nodes=None, cliques=None): |
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534 """Returns a list of cliques containing the given node. |
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535 |
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536 Returns a single list or list of lists depending on input nodes. |
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537 Optional list of cliques can be input if already computed. |
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538 """ |
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539 if cliques is None: |
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540 cliques = list(find_cliques(G)) |
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541 |
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542 if nodes is None: |
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543 nodes = list(G.nodes()) # none, get entire graph |
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544 |
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545 if not isinstance(nodes, list): # check for a list |
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546 v = nodes |
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547 # assume it is a single value |
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548 vcliques = [c for c in cliques if v in c] |
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549 else: |
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550 vcliques = {} |
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551 for v in nodes: |
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552 vcliques[v] = [c for c in cliques if v in c] |
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553 return vcliques |