fnss.topologies.randmodels.glp_topology¶

glp_topology
(n, m, m0, p, beta, seed=None)[source]¶ Return a random topology using the Generalized Linear Preference (GLP) preferential attachment model.
It differs from the extended BarabasiAlbert model in that there is link rewiring and a beta parameter is introduced to finetune preferential attachment.
More precisely, the GLP topology is built as follows. First, a line topology with m0 nodes is created. Then, at each step: with probability p, add m new links between existing nodes, selected with probability:
with probability , add a new node and attach it to m nodes of the existing topology selected with probability
Repeat the previous step until the topology comprises n nodes in total.
Parameters:  n : int
Number of nodes
 m : int
Number of edges to attach from a new node to existing nodes
 m0 : int
Number of edges initially attached to the network
 p : float
The probability that new links are added
 beta : float
Parameter to finetune preferntial attachment: beta < 1
 seed : int, optional
Seed for random number generator (default=None).
Returns:  G : Topology
References
[1] T. Bu and D. Towsey “On distinguishing between Internet power law topology generators”, Proceeding od the 21st IEEE INFOCOM conference. IEEE, volume 2, pages 638647, 2002.