Multilinear PageRank: Uniqueness, error bound and perturbation analysis

被引：15

作者：

Li, Wen ^{[1
]}

Liu, Dongdong ^{[2
]}

Vong, Seak-Weng ^{[3
]}

Xiao, Mingqing ^{[4
]}

机构：

[1] South China Normal Univ, Sch Math Sci, Guangzhou 510631, Peoples R China

[2] Guangdong Univ Technol, Sch Appl Mathematices, Guangzhou 510006, Peoples R China

[3] Univ Macau, Dept Math, Taipa, Macao, Peoples R China

[4] Southern Illinois Univ Carbondale, Dept Math, Carbondale, IL 62901 USA

来源：

APPLIED NUMERICAL MATHEMATICS | 2020年 / 156卷

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

Multilinear PageRank vector; Stochastic tensor; Uniqueness condition; Error bound; Perturbation; ORDER MARKOV-CHAINS; TRANSITION; MODEL;

D O I：

10.1016/j.apnum.2020.05.022

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we revisit the multilinear PageRank problem. Under the framework of tensor, we establish several new and tighter uniqueness conditions for the multilinear PageRank vector. Meanwhile, a refined error bound for the inverse iteration as well as the new perturbation bounds under different norms, which improve the existing ones in the current literature, are developed with feasible computations. Several numerical examples are given to validate the significant effectiveness of the proposed bounds. (C) 2020 IMACS. Published by Elsevier B.V. All rights reserved.

引用

页码：584 / 607

页数：24

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