2.2 KiB
bb_fitness(1) -- Grow a random graph with the fitness model
SYNOPSIS
bb_fitness [SHOW]
DESCRIPTION
bb_fitness grows an undirected random scale-free graph with
nodes using the fitness model proposed by Bianconi and Barabasi. The
initial network is a clique of nodes, and each new node creates
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].
PARAMETERS
-
: Number of nodes of the final graph.
-
: Number of edges created by each new node.
-
: Number of nodes in the initial (seed) graph.
-
SHOW: If the fourth parameter is equal to
SHOW, the values of node attractiveness are printed on STDERR.
OUTPUT
bb_fitness prints on STDOUT the edge list of the final graph.
EXAMPLES
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).
SEE ALSO
ba(1), dms(1)
REFERENCES
-
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)
AUTHORS
(c) Vincenzo 'KatolaZ' Nicosia 2009-2017 <v.nicosia@qmul.ac.uk>.