The PageRank model of minimal irreducible adjustment and its lumping method

DOI10.1007/S12190-012-0619-ZzbMath1308.65052OpenAlexW2026886338MaRDI QIDQ2511361

Yong-Zhong Song, Xin Chen, Lin-Lin Li

Publication date: 5 August 2014

Published in: Journal of Applied Mathematics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s12190-012-0619-z

zbMATH Keywords

Markov chainrandom matrixstochastic matrixGoogle matrixdangling nodesPageRank vectorminimal irreducibilitylumping methodnondangling nodesPageRank model

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Searching and sorting (68P10) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Numerical analysis or methods applied to Markov chains (65C40) Iterative numerical methods for linear systems (65F10) Random matrices (algebraic aspects) (15B52)

Related Items (1)

An extrapolation iteration and its lumped type iteration for computing PageRank

Cites Work

• Unnamed Item
• An Arnoldi-type algorithm for computing Page Rank
• Partial least-squares vs. Lanczos bidiagonalization. I: Analysis of a projection method for multiple regression
• An Arnoldi-extrapolation algorithm for computing pagerank
• On computing PageRank via lumping the Google matrix
• The PageRank Vector: Properties, Computation, Approximation, and Acceleration
• PageRank Computation, with Special Attention to Dangling Nodes
• Convergence Analysis of a PageRank Updating Algorithm by Langville and Meyer

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