Generate scale-free networks with desired power-law degree distributions

I'm trying to reproduce the synthetic networks (graphs) described in some
papers.

How to create scale-free networks with desired power-law degree
distributions, ? The lambda λ values are given, like:

1. n = 50000, λ = 3, 2.7, 2.3, with in a paper
2. n = 4000 and λ = 2.5, or n = 6000 and λ = 3 in the other paper

Here are some references:

1. Catastrophic cascade of failures in interdependent networks, Buldyrev et
al. 2010, with a separately provided Supplementary Information
2. Small Cluster in Cyber Physical Systems, Huang et al. 2014
3. Catastrophic cascade of failures in interdependent networks, Havlin et
al. 2010, this is on the Arxiv and somewhat clarifies the first

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Hi Weitao Han

In general I think you would want to use a generative model (see for
instance
http://tuvalu.santafe.edu/~aaronc/courses/5352/fall2013/csci5352_2013_L11.pdf
for an introduction).

You can find an implementation that is already ready in my library,
(primarily for directed networks):
https://nngt.readthedocs.io/en/latest/modules/generation.html#nngt.generation.random_scale_free

Best,
Tanguy

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