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 6 0.333333
1 3 0.333333
2 4 0.500000
3 4 0.333333
4 2 0.000000
5 5 0.300000
6 5 0.100000
7 3 0.000000
8 3 0.000000
9 5 0.300000
#/home/travis/build/networkx/networkx/examples/graph/plot_erdos_renyi.py
# GMT Mon Jan 22 07:35:58 2018
#
0 2 3 4 5 6 9
1 9 5 6
2 3 5 6
3 8 9
4 7
5 8 9
6 8 7
7 9
8
9


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

#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>

import sys

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 to terminal
try: