1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
|
ba(1) -- Grow a Barabasi-Albert scale-free random graph
======
## SYNOPSIS
`ba` <N> <m> <n0>
## DESCRIPTION
`ba` grows an undirected random scale-free graph with <N> nodes using
the linear preferential attachment model proposed by Barabasi and
Albert. The initial network is a ring of <n0> nodes, and each new node
creates <m> new edges. The resulting graph will have a scale-free
degree distribution, whose exponent converges to `gamma=3.0` for large
<N>.
## PARAMETERS
* <N>:
Number of nodes of the final graph.
* <m>:
Number of edges created by each new node.
* <n0>:
Number of nodes in the initial (seed) graph.
## OUTPUT
`ba` prints on STDOUT the edge list of the final graph.
## EXAMPLES
The following command:
$ ba 10000 3 5 > ba_10000_3_5.txt
creates a Barabasi-Albert scale-free graph with <N=10000> nodes, where
each new node creates <m=3> new edges and the initial seed network is
a ring of <n0=5> nodes. The edge list of the graph is saved in the
file `ba_10000_3_5.txt` (thanks to the redirection operator `>`).
## SEE ALSO
bb_fitness(1), dms(1), bbv(1)
## REFERENCES
* A\.-L. Barabasi, R. Albert, "Emergence of scaling in random
networks", Science 286, 509-512 (1999).
* V\. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles,
Methods and Applications", Chapter 6, Cambridge University Press
(2017)
* V\. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles,
Methods and Applications", Appendix 13, Cambridge University Press
(2017)
## AUTHORS
(c) Vincenzo 'KatolaZ' Nicosia 2009-2017 `<v.nicosia@qmul.ac.uk>`.
|
