generalized pareto random number generators  (MathWorks Inc)

Journal: Nature Communications

Article Title: Exploratory adaptation in large random networks

doi: 10.1038/ncomms14826

Figure Lengend Snippet: ( a ) Seven ensembles of networks of size N =1,500 and different topologies exhibit remarkably different convergence fractions (CFs). Ensembles are characterized by the out- and in- degree distributions of the adjacency matrix T : ‘SF', scale free distribution; ‘Exp', exponential distribution; ‘Binom', Binomial distribution. ( b ) CF as a function of network size for the same ensembles of ( a ) with matching colours. N =1,500, y =0, g 0 =10, D =10 −3 . Parameters for degree distributions: SF, ( a =1, γ =2.4); Binom, ; Exp, ( β =3.5).

Article Snippet: Exponential and Scale-free sequences are implemented by a discretization of the continuous MATLAB Exponential and Generalized Pareto random number generators with parameters k =1/( γ −1), σ = a /( γ −1) and θ = a .

Techniques:

Journal: Nature Communications

Article Title: Exploratory adaptation in large random networks

doi: 10.1038/ncomms14826

Figure Lengend Snippet: Solid lines depict stretched exponential fits. ( a ) Probability density distribution (PDF) of convergence time for three topological ensembles. ( b ) PDFs after deleting the 8 largest hubs (red) or the same number of randomly-chosen nodes (light blue) from the SF-Binom ensemble. All ensembles have N =1,000, y =0, g 0 =10, and D =10 −3 . Degree distribution parameters: SF, ( a =1, γ =2.4); Binom, ; Exp, ( ).

Article Snippet: Exponential and Scale-free sequences are implemented by a discretization of the continuous MATLAB Exponential and Generalized Pareto random number generators with parameters k =1/( γ −1), σ = a /( γ −1) and θ = a .

Techniques: