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What is the most efficient graph data structure in Python?

Create an G{n,m} random graph with n nodes and m edges
and report some properties.

This graph is sometimes called the Erd##[m~Qs-Rényi graph
but is different from G{n,p} or binomial_graph which is also
sometimes called the Erd##[m~Qs-Rényi graph.
__author__ = """Aric Hagberg ("""
__credits__ = """"""
#    Copyright (C) 2004-2006 by 
#    Aric Hagberg 
#    Dan Schult 
#    Pieter Swart 
#    Distributed under the terms of the GNU Lesser General Public License

from networkx import *
import sys

n=10 # 10 nodes
m=20 # 20 edges


# some properties
print "node degree clustering"
for v in nodes(G):
    print v,degree(G,v),clustering(G,v)

# print the adjacency list to terminal 

Tags: python performance graph-theory data-structures

Source: By bgoncalves as answer to the question

This code snippet was collected from stackoverflow, and is licensed under CC BY-SA 3.0

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