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- """Functions for generating stochastic graphs from a given weighted directed
- graph.
- """
- import networkx as nx
- from networkx.classes import DiGraph, MultiDiGraph
- from networkx.utils import not_implemented_for
- __all__ = ["stochastic_graph"]
- @not_implemented_for("undirected")
- @nx._dispatchable(
- edge_attrs="weight", mutates_input={"not copy": 1}, returns_graph=True
- )
- def stochastic_graph(G, copy=True, weight="weight"):
- """Returns a right-stochastic representation of directed graph `G`.
- A right-stochastic graph is a weighted digraph in which for each
- node, the sum of the weights of all the out-edges of that node is
- 1. If the graph is already weighted (for example, via a 'weight'
- edge attribute), the reweighting takes that into account.
- Parameters
- ----------
- G : directed graph
- A :class:`~networkx.DiGraph` or :class:`~networkx.MultiDiGraph`.
- copy : boolean, optional
- If this is True, then this function returns a new graph with
- the stochastic reweighting. Otherwise, the original graph is
- modified in-place (and also returned, for convenience).
- weight : edge attribute key (optional, default='weight')
- Edge attribute key used for reading the existing weight and
- setting the new weight. If no attribute with this key is found
- for an edge, then the edge weight is assumed to be 1. If an edge
- has a weight, it must be a positive number.
- """
- if copy:
- G = MultiDiGraph(G) if G.is_multigraph() else DiGraph(G)
- # There is a tradeoff here: the dictionary of node degrees may
- # require a lot of memory, whereas making a call to `G.out_degree`
- # inside the loop may be costly in computation time.
- degree = dict(G.out_degree(weight=weight))
- for u, v, d in G.edges(data=True):
- if degree[u] == 0:
- d[weight] = 0
- else:
- d[weight] = d.get(weight, 1) / degree[u]
- nx._clear_cache(G)
- return G
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