Choose the damping, choose the ranking?
Journal of Discrete Algorithms
Tracking the random surfer: empirically measured teleportation parameters in PageRank
Proceedings of the 19th international conference on World wide web
Arnoldi versus GMRES for computing pageRank: A theoretical contribution to google's pageRank problem
ACM Transactions on Information Systems (TOIS)
An Inner-Outer Iteration for Computing PageRank
SIAM Journal on Scientific Computing
Local computation of PageRank: the ranking side
Proceedings of the 20th ACM international conference on Information and knowledge management
Ergodicity Coefficients Defined by Vector Norms
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Scientific Computing
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We present computationally efficient criteria that can guarantee correct ordinal ranking of Google's PageRank scores when they are computed with the power method (ordinal ranking of a list consists of assigning an ordinal number to each item in the list). We discuss the tightness of the ranking criteria, and illustrate their effectiveness for top k and bucket ranking. We present a careful implementation of the power method, combined with a roundoff error analysis that is valid for matrix dimensions $n