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Note

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

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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 ( 0)
35 ( 2) **
36 ( 0)
37 ( 7) *******
38 ( 4) ****
39 ( 9) *********
40 (10) **********
41 (13) *************
42 (21) *********************
43 (19) *******************
44 (21) *********************
45 (27) ***************************
46 (28) ****************************
47 (28) ****************************
48 (32) ********************************
49 (27) ***************************
50 (25) *************************
51 (16) ****************
52 (21) *********************
53 (36) ************************************
54 (27) ***************************
55 (18) ******************
56 (19) *******************
57 (15) ***************
58 (15) ***************
59 (16) ****************
60 (12) ************
61 ( 3) ***
62 ( 8) ********
63 ( 9) *********
64 ( 2) **
65 ( 2) **
66 ( 4) ****
67 ( 2) **
68 ( 0)
69 ( 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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