Return a random topology using the Generalized Linear Prefernece (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
m : int
m0 : int
p : float
beta : float
seed : int, optional
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Returns : | G : Topology |
References
[R21] | 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. |