Note
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Expected Degree Sequence¶
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 ( 2) **
33 ( 0)
34 ( 2) **
35 ( 2) **
36 ( 2) **
37 ( 7) *******
38 ( 4) ****
39 (14) **************
40 ( 5) *****
41 (14) **************
42 (22) **********************
43 (25) *************************
44 (11) ***********
45 (23) ***********************
46 (24) ************************
47 (20) ********************
48 (36) ************************************
49 (28) ****************************
50 (31) *******************************
51 (21) *********************
52 (31) *******************************
53 (28) ****************************
54 (43) *******************************************
55 (21) *********************
56 (16) ****************
57 (18) ******************
58 ( 6) ******
59 ( 8) ********
60 (10) **********
61 (12) ************
62 ( 7) *******
63 ( 2) **
64 ( 4) ****
65 ( 1) *
import networkx as nx
# 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 = nx.expected_degree_graph(w) # configuration model
print("Degree histogram")
print("degree (#nodes) ****")
dh = nx.degree_histogram(G)
for i, d in enumerate(dh):
print(f"{i:2} ({d:2}) {'*'*d}")
Total running time of the script: ( 0 minutes 0.046 seconds)