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.

../../_images/sphx_glr_plot_erdos_renyi_001.png

Out:

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

Total running time of the script: ( 0 minutes 0.025 seconds)

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