A Hessenberg-type Algorithm for Computing PageRank Problems
Abstract
PageRank is a widespread model for analysing the relative relevance of nodes within large graphs arising in several applications. In the current paper, we present a cost-effective Hessenberg-type method built upon the Hessenberg process for the solution of difficult PageRank problems. The new method is very competitive with other popular algorithms in this field, such as Arnoldi-type methods, especially when the damping factor is close to and the dimension of the search subspace is large. The convergence and the complexity of the proposed algorithm are investigated. Numerical experiments are reported to show the efficiency of the new solver for practical PageRank computations.
Cite
@article{arxiv.1908.00235,
title = {A Hessenberg-type Algorithm for Computing PageRank Problems},
author = {Xian-Ming Gu and Siu-Long Lei and Ke Zhang and Zhao-Li Shen and Chun Wen and Bruno Carpentieri},
journal= {arXiv preprint arXiv:1908.00235},
year = {2023}
}
Comments
4 Figures, 6 Tables. 19 pages, the current version has been improved further and accepted by {\em Numerical Algorithms}