bb_fitness
- Grow a random graph with the fitness model
bb_fitness
N m n0 [SHOW]
bb_fitness
grows an undirected random scale-free graph with N
nodes using the fitness model proposed by Bianconi and Barabasi. The
initial network is a clique of n0 nodes, and each new node creates
m new edges. The probability that a new node create an edge to node
j
is proportional to
a_j * k_j
where a_j
is the attractiveness (fitness) of node j
. The values of
node attractiveness are sampled uniformly in the interval [0,1].
SHOW
, the values of node
attractiveness are printed on STDERR.bb_fitness
prints on STDOUT the edge list of the final graph.
The following command:
$ bb_fitness 10000 3 4 > bb_fitness_10000_3_4.txt
uses the fitness model to create a random 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 resulting
graph is saved in the file bb_fitness_10000_3_4.txt
(notice the
redirection operator >
). The command:
$ bb_fitness 10000 3 4 SHOW > bb_fitness_10000_3_4.txt 2> bb_fitness_10000_3_4.txt_fitness
will do the same as above, but it will additionally save the values of
node fitness in the file bb_fitness_10000_3_4.txt_fitness
(notice
the redirection operator 2>
, that redirects the STDERR to the
specified file).
ba(1), dms(1)
G. Bianconi, A.-L. Barabasi, " Competition and multiscaling in evolving networks". EPL-Europhys. Lett. 54 (2001), 436.
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)
(c) Vincenzo 'KatolaZ' Nicosia 2009-2017 <v.nicosia@qmul.ac.uk>
.