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].
Number of nodes of the final graph.
Number of edges created by each new node.
Number of nodes in the initial (seed) graph.
If the fourth parameter is equal to 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>.