Paper detail

On scale-free and poly-scale behaviors of random hierarchical network

In this paper the question about statistical properties of block--hierarchical random matrices is raised for the first time in connection with structural characteristics of random hierarchical networks obtained by mipmapping procedure. In particular, we compute numerically the spectral density of large random adjacency matrices defined by a hierarchy of the Bernoulli distributions $\{q_1,q_2,...\}$ on matrix elements, where $q_γ$ depends on hierarchy level $γ$ as $q_γ=p^{-μγ}$ ($μ>0$). For the spectral density we clearly see the free--scale behavior. We show also that for the Gaussian distributions on matrix elements with zero mean and variances $σ_γ=p^{-νγ}$, the tail of the spectral density, $ρ_G(λ)$, behaves as $ρ_G(λ) \sim |λ|^{-(2-ν)/(1-ν)}$ for $|λ|\to\infty$ and $0<ν<1$, while for $ν\ge 1$ the power--law behavior is terminated. We also find that the vertex degree distribution of such hierarchical networks has a poly--scale fractal behavior extended to a very broad range of scales.