| """Stocastic graph.""" |
| # Copyright (C) 2010-2013 by |
| # Aric Hagberg <hagberg@lanl.gov> |
| # Dan Schult <dschult@colgate.edu> |
| # Pieter Swart <swart@lanl.gov> |
| # All rights reserved. |
| # BSD license. |
| import networkx as nx |
| __author__ = "Aric Hagberg <aric.hagberg@gmail.com>" |
| __all__ = ['stochastic_graph'] |
| |
| def stochastic_graph(G, copy=True, weight='weight'): |
| """Return a right-stochastic representation of G. |
| |
| A right-stochastic graph is a weighted digraph in which all of |
| the node (out) neighbors edge weights sum to 1. |
| |
| Parameters |
| ----------- |
| G : graph |
| A NetworkX graph |
| |
| copy : boolean, optional |
| If True make a copy of the graph, otherwise modify the original graph |
| |
| weight : edge attribute key (optional, default='weight') |
| Edge data key used for weight. If no attribute is found for an edge |
| the edge weight is set to 1. |
| """ |
| if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph: |
| raise nx.NetworkXError('stochastic_graph not implemented ' |
| 'for multigraphs') |
| |
| if not G.is_directed(): |
| raise nx.NetworkXError('stochastic_graph not implemented ' |
| 'for undirected graphs') |
| |
| if copy: |
| W = nx.DiGraph(G) |
| else: |
| W = G # reference original graph, no copy |
| |
| degree = W.out_degree(weight=weight) |
| for (u,v,d) in W.edges(data=True): |
| d[weight] = float(d.get(weight,1.0))/degree[u] |
| return W |
