Note
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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 ( 1) *
33 ( 0)
34 ( 3) ***
35 ( 2) **
36 ( 2) **
37 ( 3) ***
38 ( 4) ****
39 (12) ************
40 ( 8) ********
41 ( 8) ********
42 ( 9) *********
43 (21) *********************
44 (16) ****************
45 (28) ****************************
46 (28) ****************************
47 (30) ******************************
48 (29) *****************************
49 (29) *****************************
50 (20) ********************
51 (30) ******************************
52 (24) ************************
53 (29) *****************************
54 (21) *********************
55 (31) *******************************
56 (19) *******************
57 (17) *****************
58 (10) **********
59 (16) ****************
60 (10) **********
61 (11) ***********
62 ( 6) ******
63 ( 6) ******
64 ( 5) *****
65 ( 4) ****
66 ( 1) *
67 ( 3) ***
68 ( 1) *
69 ( 1) *
70 ( 1) *
71 ( 0)
72 ( 0)
73 ( 0)
74 ( 0)
75 ( 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 0.096 seconds)