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 ( 2) **
34 ( 3) ***
35 ( 1) *
36 ( 6) ******
37 ( 3) ***
38 ( 5) *****
39 (12) ************
40 (13) *************
41 ( 8) ********
42 (13) *************
43 (19) *******************
44 (18) ******************
45 (23) ***********************
46 (21) *********************
47 (27) ***************************
48 (42) ******************************************
49 (30) ******************************
50 (25) *************************
51 (32) ********************************
52 (30) ******************************
53 (19) *******************
54 (18) ******************
55 (20) ********************
56 (23) ***********************
57 (15) ***************
58 (19) *******************
59 (12) ************
60 ( 7) *******
61 ( 3) ***
62 ( 8) ********
63 ( 7) *******
64 ( 4) ****
65 ( 1) *
66 ( 4) ****
67 ( 1) *
68 ( 2) **
69 ( 2) **
70 ( 0)
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 0.037 seconds)