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This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Erdos Renyi

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

This graph is sometimes called the Erdős-Rényi graph but is different from G{n,p} or binomial_graph which is also sometimes called the Erdős-Rényi graph.

Out:

node degree clustering
0 3 0.000000
1 4 0.333333
2 6 0.400000
3 4 0.666667
4 3 0.666667
5 3 0.333333
6 4 0.500000
7 4 0.500000
8 2 1.000000
9 7 0.380952
0 7 5 1
1 3 6 9
2 6 9 4 7 5 3
3 7 9
4 5 9
5
6 8 9
7 9
8 9
9

# Author: Aric Hagberg (hagberg@lanl.gov)

#    Copyright (C) 2004-2019 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.

import matplotlib.pyplot as plt
from networkx import nx

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

G = nx.gnm_random_graph(n, m)

# some properties
print("node degree clustering")
for v in nx.nodes(G):
    print('%s %d %f' % (v, nx.degree(G, v), nx.clustering(G, v)))

# print the adjacency list
for line in nx.generate_adjlist(G):
    print(line)

nx.draw(G)
plt.show()