bbv - Grow a weighted scale-free random graph
bbv N m n0 w0 delta
bbv grows an undirected weighted random scale-free graph with N
nodes using the model proposed by Barrat, Barthelemy, and
Vespignani. The initial network is a clique of n0 nodes, and each
new node creates m new edges, each with weight w0. The parameter
delta sets the amount of weight to be redistributed in the
neighbourhood of newly-connected nodes.
Number of nodes of the final graph.
Number of edges created by each new node.
Number of nodes in the initial (seed) graph.
Weight of each new edge (must be >=0)
The amount of weight to be redistributed among the neighbours of newly-connected nodes.
bbv prints on STDOUT the edge list of the final graph, which
consists of three columns:
node1 node2 weight
where weight is the weight of the corresponding edge. Please note
that each edge is printed only once.
The following command:
$ bbv 10000 3 5 1.0 0.5 > bbv_10000_3_5_1.0_0.5.txt
creates a weighted 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. Each new edge has an initial weight equal to
w0. The weights of existing edges are rearranged after the addition
of a new edge, by rearranging an amount of weight equal to delta.
The final graph is saved in the file bbv_10000_3_5_1.0_0.5.txt
(notice the STDOUT redirection operator >).
ba(1), bb_fitness(1), dms(1)
A. Barrat, M. Barthelemy, and A. Vespignani. "Weighted Evolving Networks: Coupling Topology and Weight Dynamics". Phys. Rev. Lett. 92 (2004), 228701.
A. Barrat, M. Barthelemy, and A. Vespignani. "Modeling the evolution of weighted networks". Phys. Rev. E 70 (2004), 066149.
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>.