Matrix analysis
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Distributed Algorithms
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
PageRank as a function of the damping factor
WWW '05 Proceedings of the 14th international conference on World Wide Web
TruRank: taking PageRank to the limit
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Jordan Canonical Form of the Google Matrix: A Potential Contribution to the PageRank Computation
SIAM Journal on Matrix Analysis and Applications
Convergence Analysis of a PageRank Updating Algorithm by Langville and Meyer
SIAM Journal on Matrix Analysis and Applications
Numerical Methods for Structured Markov Chains (Numerical Mathematics and Scientific Computation)
Numerical Methods for Structured Markov Chains (Numerical Mathematics and Scientific Computation)
Generalizing PageRank: damping functions for link-based ranking algorithms
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
The PageRank Vector: Properties, Computation, Approximation, and Acceleration
SIAM Journal on Matrix Analysis and Applications
Comments on "Jordan Canonical Form of the Google Matrix"
SIAM Journal on Matrix Analysis and Applications
Accelerating the Arnoldi-Type Algorithm for the PageRank Problem and the ProteinRank Problem
Journal of Scientific Computing
| Hi-index | 7.29 |
The spectral and Jordan structures of the Web hyperlink matrix G(c)=cG+(1-c)ev^T have been analyzed when G is the basic (stochastic) Google matrix, c is a real parameter such that 0"1y(c)=Nv for all v; this limit may fail to exist for some v if @l is not semisimple. As a special case of our results, we obtain a complex analog of PageRank for the Web hyperlink matrix G(c) with a complex parameter c. We study regularity, limits, expansions, and conditioning of y(c) and we propose algorithms (e.g., complex extrapolation, power method on a modified matrix etc.) that may provide an efficient way to compute PageRank also with c close or equal to 1. An interpretation of the limit vector Nv and a related critical discussion on the model, on its adherence to reality, and possible ways for its improvement, represent the contribution of the paper on modeling issues.