Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/generators/cographs.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 r"""Generators for cographs |
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2 |
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3 A cograph is a graph containing no path on four vertices. |
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4 Cographs or $P_4$-free graphs can be obtained from a single vertex |
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5 by disjoint union and complementation operations. |
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6 |
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7 References |
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8 ---------- |
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9 .. [0] D.G. Corneil, H. Lerchs, L.Stewart Burlingham, |
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10 "Complement reducible graphs", |
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11 Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174, |
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12 ISSN 0166-218X. |
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13 """ |
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14 import networkx as nx |
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15 from networkx.utils import py_random_state |
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16 |
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17 __all__ = ["random_cograph"] |
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18 |
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19 |
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20 @py_random_state(1) |
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21 def random_cograph(n, seed=None): |
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22 r"""Returns a random cograph with $2 ^ n$ nodes. |
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23 |
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24 A cograph is a graph containing no path on four vertices. |
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25 Cographs or $P_4$-free graphs can be obtained from a single vertex |
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26 by disjoint union and complementation operations. |
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27 |
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28 This generator starts off from a single vertex and performes disjoint |
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29 union and full join operations on itself. |
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30 The decision on which operation will take place is random. |
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31 |
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32 Parameters |
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33 ---------- |
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34 n : int |
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35 The order of the cograph. |
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36 seed : integer, random_state, or None (default) |
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37 Indicator of random number generation state. |
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38 See :ref:`Randomness<randomness>`. |
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39 |
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40 Returns |
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41 ------- |
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42 G : A random graph containing no path on four vertices. |
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43 |
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44 See Also |
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45 -------- |
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46 full_join |
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47 union |
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48 |
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49 References |
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50 ---------- |
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51 .. [1] D.G. Corneil, H. Lerchs, L.Stewart Burlingham, |
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52 "Complement reducible graphs", |
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53 Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174, |
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54 ISSN 0166-218X. |
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55 """ |
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56 R = nx.empty_graph(1) |
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57 |
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58 for i in range(n): |
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59 RR = nx.relabel_nodes(R.copy(), lambda x: x + len(R)) |
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60 |
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61 if seed.randint(0, 1) == 0: |
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62 R = nx.full_join(R, RR) |
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63 else: |
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64 R = nx.disjoint_union(R, RR) |
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65 |
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66 return R |