Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

# 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 ( 0)
35 ( 2) **
36 ( 3) ***
37 ( 7) *******
38 ( 6) ******
39 ( 4) ****
40 (11) ***********
41 (16) ****************
42 (10) **********
43 (21) *********************
44 (14) **************
45 (20) ********************
46 (34) **********************************
47 (32) ********************************
48 (29) *****************************
49 (27) ***************************
50 (28) ****************************
51 (27) ***************************
52 (25) *************************
53 (29) *****************************
54 (27) ***************************
55 (21) *********************
56 (20) ********************
57 (16) ****************
58 (11) ***********
59 (11) ***********
60 (11) ***********
61 ( 9) *********
62 ( 4) ****
63 (10) **********
64 ( 2) **
65 ( 3) ***
66 ( 3) ***
67 ( 1) *
68 ( 2) **
69 ( 1) *
70 ( 1) *
71 ( 0)
72 ( 0)
73 ( 1) *
74 ( 1) *


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(f"{i:2} ({d:2}) {'*'*d}")


Total running time of the script: ( 0 minutes 0.035 seconds)

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