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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 ( 2) **
31 ( 0)
32 ( 0)
33 ( 2) **
34 ( 0)
35 ( 1) *
36 ( 4) ****
37 ( 4) ****
38 ( 6) ******
39 (10) **********
40 ( 7) *******
41 (11) ***********
42 (11) ***********
43 (15) ***************
44 (21) *********************
45 (24) ************************
46 (28) ****************************
47 (25) *************************
48 (29) *****************************
49 (38) **************************************
50 (35) ***********************************
51 (29) *****************************
52 (26) **************************
53 (36) ************************************
54 (19) *******************
55 (19) *******************
56 (14) **************
57 (18) ******************
58 (15) ***************
59 ( 8) ********
60 (11) ***********
61 ( 7) *******
62 (11) ***********
63 ( 6) ******
64 ( 2) **
65 ( 1) *
66 ( 3) ***
67 ( 0)
68 ( 2) **

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 0.027 seconds)

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