Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/generators/stochastic.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 """Functions for generating stochastic graphs from a given weighted directed |
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2 graph. |
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3 |
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4 """ |
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5 |
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6 from networkx.classes import DiGraph |
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7 from networkx.classes import MultiDiGraph |
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8 from networkx.utils import not_implemented_for |
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9 |
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10 __all__ = ["stochastic_graph"] |
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11 |
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12 |
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13 @not_implemented_for("undirected") |
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14 def stochastic_graph(G, copy=True, weight="weight"): |
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15 """Returns a right-stochastic representation of directed graph `G`. |
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16 |
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17 A right-stochastic graph is a weighted digraph in which for each |
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18 node, the sum of the weights of all the out-edges of that node is |
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19 1. If the graph is already weighted (for example, via a 'weight' |
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20 edge attribute), the reweighting takes that into account. |
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21 |
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22 Parameters |
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23 ---------- |
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24 G : directed graph |
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25 A :class:`~networkx.DiGraph` or :class:`~networkx.MultiDiGraph`. |
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26 |
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27 copy : boolean, optional |
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28 If this is True, then this function returns a new graph with |
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29 the stochastic reweighting. Otherwise, the original graph is |
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30 modified in-place (and also returned, for convenience). |
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31 |
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32 weight : edge attribute key (optional, default='weight') |
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33 Edge attribute key used for reading the existing weight and |
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34 setting the new weight. If no attribute with this key is found |
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35 for an edge, then the edge weight is assumed to be 1. If an edge |
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36 has a weight, it must be a a positive number. |
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37 |
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38 """ |
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39 if copy: |
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40 G = MultiDiGraph(G) if G.is_multigraph() else DiGraph(G) |
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41 # There is a tradeoff here: the dictionary of node degrees may |
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42 # require a lot of memory, whereas making a call to `G.out_degree` |
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43 # inside the loop may be costly in computation time. |
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44 degree = dict(G.out_degree(weight=weight)) |
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45 for u, v, d in G.edges(data=True): |
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46 if degree[u] == 0: |
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47 d[weight] = 0 |
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48 else: |
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49 d[weight] = d.get(weight, 1) / degree[u] |
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50 return G |