fnss.topologies.randmodels.glp_topology¶
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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 Barabasi-Albert model in that there is link rewiring and a beta parameter is introduced to fine-tune 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 fine-tune preferntial attachment: beta < 1
seed : int, optional
Seed for random number generator (default=None).
Returns: G : Topology
References
[R4343] T. Bu and D. Towsey “On distinguishing between Internet power law topology generators”, Proceeding od the 21st IEEE INFOCOM conference. IEEE, volume 2, pages 638-647, 2002.