Dorogovtsev S N, Goltsev A V, Mendes J F F, Samukhin A N
Departamento de Física and Centro de Física do Porto, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal.
Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Oct;68(4 Pt 2):046109. doi: 10.1103/PhysRevE.68.046109. Epub 2003 Oct 10.
We propose a general approach to the description of spectra of complex networks. For the spectra of networks with uncorrelated vertices (and a local treelike structure), exact equations are derived. These equations are generalized to the case of networks with correlations between neighboring vertices. The tail of the density of eigenvalues rho(lambda) at large /lambda/ is related to the behavior of the vertex degree distribution P(k) at large k. In particular, as P(k) approximately k(-gamma), rho(lambda) approximately /lambda/(1-2 gamma). We propose a simple approximation, which enables us to calculate spectra of various graphs analytically. We analyze spectra of various complex networks and discuss the role of vertices of low degree. We show that spectra of locally treelike random graphs may serve as a starting point in the analysis of spectral properties of real-world networks, e.g., of the Internet.
我们提出了一种描述复杂网络频谱的通用方法。对于具有不相关顶点(以及局部树状结构)的网络频谱,推导了精确方程。这些方程被推广到相邻顶点之间存在相关性的网络情况。在大的(\vert\lambda\vert)时,特征值密度(\rho(\lambda))的尾部与大(k)时顶点度分布(P(k))的行为相关。特别地,当(P(k)\approx k^{-\gamma})时,(\rho(\lambda)\approx\vert\lambda\vert^{1 - 2\gamma})。我们提出了一种简单的近似方法,它使我们能够解析地计算各种图的频谱。我们分析了各种复杂网络的频谱,并讨论了低度顶点的作用。我们表明,局部树状随机图的频谱可以作为分析现实世界网络(例如互联网)频谱特性的起点。