Expected Degree SequenceΒΆ

Random graph from given degree sequence.

Out:

Degree histogram
degree (#nodes) ****
 0 ( 0)
 1 ( 0)
 2 ( 0)
 3 ( 0)
 4 ( 0)
 5 ( 0)
 6 ( 0)
 7 ( 0)
 8 ( 0)
 9 ( 0)
10 ( 0)
11 ( 0)
12 ( 0)
13 ( 0)
14 ( 0)
15 ( 0)
16 ( 0)
17 ( 0)
18 ( 0)
19 ( 0)
20 ( 0)
21 ( 0)
22 ( 0)
23 ( 0)
24 ( 0)
25 ( 0)
26 ( 0)
27 ( 0)
28 ( 0)
29 ( 0)
30 ( 0)
31 ( 0)
32 ( 0)
33 ( 2) **
34 ( 2) **
35 ( 3) ***
36 ( 3) ***
37 ( 3) ***
38 ( 3) ***
39 (10) **********
40 (10) **********
41 (13) *************
42 (16) ****************
43 (16) ****************
44 (14) **************
45 (27) ***************************
46 (23) ***********************
47 (28) ****************************
48 (24) ************************
49 (21) *********************
50 (25) *************************
51 (29) *****************************
52 (30) ******************************
53 (17) *****************
54 (30) ******************************
55 (30) ******************************
56 (22) **********************
57 (22) **********************
58 (14) **************
59 (17) *****************
60 ( 8) ********
61 (11) ***********
62 ( 7) *******
63 ( 2) **
64 ( 2) **
65 ( 4) ****
66 ( 7) *******
67 ( 2) **
68 ( 0)
69 ( 0)
70 ( 2) **
71 ( 1) *

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

#    Copyright (C) 2006-2019 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.

import networkx as nx
from networkx.generators.degree_seq import expected_degree_graph

# make a random graph of 500 nodes with expected degrees of 50
n = 500  # n nodes
p = 0.1
w = [p * n for i in range(n)]  # w = p*n for all nodes
G = expected_degree_graph(w)  # configuration model
print("Degree histogram")
print("degree (#nodes) ****")
dh = nx.degree_histogram(G)
for i, d in enumerate(dh):
    print("%2s (%2s) %s" % (i, d, '*'*d))

Total running time of the script: ( 0 minutes 0.045 seconds)

Gallery generated by Sphinx-Gallery