Response to this SOCNET post <http://lists.ufl.edu/cgi-bin/wa?A2=ind1303&L=SOCNET&T=0&F=&S=&P=185750> Copyright (C) 2013 Adam Obeng Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limit…

graph.dot

strict digraph "fast_gnp_random_graph(10,0.2)" {
    1 -> 0	 [weight=0.85784681863];
	1 -> 9	 [weight=0.446412404196];
	1 -> 6	 [weight=0.154317919969];
	2 -> 5	 [weight=0.625265147359];
	2 -> 6	 [weight=0.646490491015];
	3 -> 0	 [weight=0.0314493433996];
	3 -> 1	 [weight=0.679579531344];
	4 -> 5	 [weight=0.839968183284];
	5 -> 1	 [weight=0.325702378388];
	5 -> 6	 [weight=0.202474697063];
	5 -> 7	 [weight=0.541832673518];
	6 -> 0	 [weight=0.896564262162];
	6 -> 7	 [weight=0.217043461536];
	7 -> 0	 [weight=0.305489723841];
	7 -> 1	 [weight=0.858152144914];
	7 -> 8	 [weight=0.142028663227];
	7 -> 9	 [weight=0.946092301818];
	9 -> 8	 [weight=0.626423028336];
}

random_directed_weighted_graph.py

import networkx as nx
import random

n = 10  # `n` is the number of nodes
p = 0.2  # p is the probability of edge existing

G = nx.fast_gnp_random_graph(n, p, directed=True)

for e in G.edges_iter():
    #  We're not actually adding edges, just updating the weight on all of the
    #  edges which already exist
    
    #  The `[e]` is because add_edges_from() expects a list of edge tuples...
    G.add_edges_from([e], weight=random.random())

nx.write_dot(G, 'graph.dot')