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具有幂律衰减键强度的随机网络中的多重分形

Multifractality in random networks with power-law decaying bond strengths.

作者信息

Vega-Oliveros Didier A, Méndez-Bermúdez J A, Rodrigues Francisco A

机构信息

Departamento de Computação e Matemáticas, Faculdade de Filosofia Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, CEP 14040-901, Ribeirão Preto, Sãu Paulo, Brasil.

School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, USA.

出版信息

Phys Rev E. 2019 Apr;99(4-1):042303. doi: 10.1103/PhysRevE.99.042303.

Abstract

In this paper we demonstrate numerically that random networks whose adjacency matrices A are represented by a diluted version of the power-law banded random matrix (PBRM) model have multifractal eigenfunctions. The PBRM model describes one-dimensional samples with random long-range bonds. The bond strengths of the model, which decay as a power-law, are tuned by the parameter μ as A_{mn}∝|m-n|^{-μ}; while the sparsity is driven by the average network connectivity α: for α=0 the vertices in the network are isolated and for α=1 the network is fully connected and the PBRM model is recovered. Though it is known that the PBRM model has multifractal eigenfunctions at the critical value μ=μ_{c}=1, we clearly show [from the scaling of the relative fluctuation of the participation number I_{2} as well as the scaling of the probability distribution functions P(lnI_{2})] the existence of the critical value μ_{c}≡μ_{c}(α) for α<1. Moreover, we characterize the multifractality of the eigenfunctions of our random network model by the use of the corresponding multifractal dimensions D_{q}, that we compute from the finite network-size scaling of the typical eigenfunction participation numbers exp〈lnI_{q}〉.

摘要

在本文中,我们通过数值方法证明,其邻接矩阵(A)由幂律带状随机矩阵(PBRM)模型的稀释版本表示的随机网络具有多重分形本征函数。PBRM模型描述具有随机长程键的一维样本。该模型的键强度按幂律衰减,由参数(\mu)调节,即(A_{mn}\propto|m - n|^{-\mu});而稀疏性由平均网络连通性(\alpha)驱动:对于(\alpha = 0),网络中的顶点是孤立的,对于(\alpha = 1),网络是完全连通的,此时恢复为PBRM模型。尽管已知PBRM模型在临界值(\mu=\mu_{c}=1)时具有多重分形本征函数,但我们[从参与数(I_{2})的相对涨落的标度以及概率分布函数(P(\ln I_{2}))的标度]清楚地表明,对于(\alpha\lt1),存在临界值(\mu_{c}\equiv\mu_{c}(\alpha))。此外,我们通过使用相应的多重分形维数(D_{q})来表征我们随机网络模型本征函数的多重分形性,我们从典型本征函数参与数(\exp\langle\ln I_{q}\rangle)的有限网络尺寸标度计算出该维数。

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