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 ( 0)
34 ( 1) *
35 ( 1) *
36 ( 1) *
37 ( 1) *
38 ( 7) *******
39 ( 8) ********
40 ( 9) *********
41 ( 8) ********
42 (13) *************
43 (19) *******************
44 (18) ******************
45 (24) ************************
46 (24) ************************
47 (24) ************************
48 (28) ****************************
49 (32) ********************************
50 (30) ******************************
51 (33) *********************************
52 (29) *****************************
53 (27) ***************************
54 (23) ***********************
55 (22) **********************
56 (21) *********************
57 (24) ************************
58 (10) **********
59 (14) **************
60 (16) ****************
61 ( 7) *******
62 ( 5) *****
63 ( 5) *****
64 ( 4) ****
65 ( 5) *****
66 ( 3) ***
67 ( 0)
68 ( 1) *
69 ( 0)
70 ( 0)
71 ( 1) *
72 ( 1) *
73 ( 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 0.037 seconds)

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