Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/generators/intersection.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 """ |
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2 Generators for random intersection graphs. |
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3 """ |
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4 import networkx as nx |
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5 from networkx.algorithms import bipartite |
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6 from networkx.utils import py_random_state |
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7 |
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8 __all__ = [ |
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9 "uniform_random_intersection_graph", |
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10 "k_random_intersection_graph", |
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11 "general_random_intersection_graph", |
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12 ] |
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13 |
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14 |
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15 @py_random_state(3) |
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16 def uniform_random_intersection_graph(n, m, p, seed=None): |
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17 """Returns a uniform random intersection graph. |
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18 |
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19 Parameters |
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20 ---------- |
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21 n : int |
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22 The number of nodes in the first bipartite set (nodes) |
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23 m : int |
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24 The number of nodes in the second bipartite set (attributes) |
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25 p : float |
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26 Probability of connecting nodes between bipartite sets |
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27 seed : integer, random_state, or None (default) |
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28 Indicator of random number generation state. |
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29 See :ref:`Randomness<randomness>`. |
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30 |
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31 See Also |
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32 -------- |
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33 gnp_random_graph |
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34 |
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35 References |
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36 ---------- |
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37 .. [1] K.B. Singer-Cohen, Random Intersection Graphs, 1995, |
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38 PhD thesis, Johns Hopkins University |
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39 .. [2] Fill, J. A., Scheinerman, E. R., and Singer-Cohen, K. B., |
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40 Random intersection graphs when m = !(n): |
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41 An equivalence theorem relating the evolution of the g(n, m, p) |
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42 and g(n, p) models. Random Struct. Algorithms 16, 2 (2000), 156–176. |
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43 """ |
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44 G = bipartite.random_graph(n, m, p, seed) |
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45 return nx.projected_graph(G, range(n)) |
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46 |
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47 |
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48 @py_random_state(3) |
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49 def k_random_intersection_graph(n, m, k, seed=None): |
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50 """Returns a intersection graph with randomly chosen attribute sets for |
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51 each node that are of equal size (k). |
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52 |
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53 Parameters |
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54 ---------- |
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55 n : int |
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56 The number of nodes in the first bipartite set (nodes) |
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57 m : int |
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58 The number of nodes in the second bipartite set (attributes) |
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59 k : float |
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60 Size of attribute set to assign to each node. |
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61 seed : integer, random_state, or None (default) |
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62 Indicator of random number generation state. |
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63 See :ref:`Randomness<randomness>`. |
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64 |
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65 See Also |
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66 -------- |
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67 gnp_random_graph, uniform_random_intersection_graph |
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68 |
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69 References |
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70 ---------- |
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71 .. [1] Godehardt, E., and Jaworski, J. |
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72 Two models of random intersection graphs and their applications. |
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73 Electronic Notes in Discrete Mathematics 10 (2001), 129--132. |
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74 """ |
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75 G = nx.empty_graph(n + m) |
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76 mset = range(n, n + m) |
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77 for v in range(n): |
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78 targets = seed.sample(mset, k) |
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79 G.add_edges_from(zip([v] * len(targets), targets)) |
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80 return nx.projected_graph(G, range(n)) |
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81 |
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82 |
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83 @py_random_state(3) |
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84 def general_random_intersection_graph(n, m, p, seed=None): |
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85 """Returns a random intersection graph with independent probabilities |
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86 for connections between node and attribute sets. |
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87 |
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88 Parameters |
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89 ---------- |
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90 n : int |
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91 The number of nodes in the first bipartite set (nodes) |
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92 m : int |
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93 The number of nodes in the second bipartite set (attributes) |
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94 p : list of floats of length m |
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95 Probabilities for connecting nodes to each attribute |
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96 seed : integer, random_state, or None (default) |
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97 Indicator of random number generation state. |
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98 See :ref:`Randomness<randomness>`. |
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99 |
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100 See Also |
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101 -------- |
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102 gnp_random_graph, uniform_random_intersection_graph |
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103 |
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104 References |
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105 ---------- |
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106 .. [1] Nikoletseas, S. E., Raptopoulos, C., and Spirakis, P. G. |
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107 The existence and efficient construction of large independent sets |
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108 in general random intersection graphs. In ICALP (2004), J. D´ıaz, |
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109 J. Karhum¨aki, A. Lepist¨o, and D. Sannella, Eds., vol. 3142 |
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110 of Lecture Notes in Computer Science, Springer, pp. 1029–1040. |
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111 """ |
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112 if len(p) != m: |
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113 raise ValueError("Probability list p must have m elements.") |
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114 G = nx.empty_graph(n + m) |
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115 mset = range(n, n + m) |
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116 for u in range(n): |
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117 for v, q in zip(mset, p): |
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118 if seed.random() < q: |
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119 G.add_edge(u, v) |
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120 return nx.projected_graph(G, range(n)) |