# 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 4 0.5
1 5 0.4
2 4 0.6666666666666666
3 5 0.6
4 2 0
5 5 0.3
6 6 0.4
7 2 0
8 5 0.4
9 2 0

the adjacency list
0 6 1 8 5
1 5 6 3 7
2 5 8 3 6
3 5 6 8
4 6 9
5 9
6 8
7 8
8
9


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(f"{v} {nx.degree(G, v)} {nx.clustering(G, v)}")

print()
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 0.144 seconds)

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