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 ( 1) *
32 ( 2) **
33 ( 0)
34 ( 0)
35 ( 0)
36 ( 3) ***
37 ( 4) ****
38 ( 5) *****
39 ( 5) *****
40 ( 7) *******
41 (14) **************
42 (15) ***************
43 (17) *****************
44 (23) ***********************
45 (24) ************************
46 (25) *************************
47 (24) ************************
48 (24) ************************
49 (38) **************************************
50 (34) **********************************
51 (34) **********************************
52 (27) ***************************
53 (26) **************************
54 (24) ************************
55 (28) ****************************
56 (23) ***********************
57 (14) **************
58 (11) ***********
59 (11) ***********
60 ( 9) *********
61 ( 9) *********
62 ( 4) ****
63 ( 3) ***
64 ( 3) ***
65 ( 3) ***
66 ( 2) **
67 ( 2) **
68 ( 1) *
69 ( 0)
70 ( 0)
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 0.086 seconds)