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Extrapolation Methods for Accelerating PageRank Computations
 In Proceedings of the Twelfth International World Wide Web Conference
, 2003
"... We present a novel algorithm for the fast computation of PageRank, a hyperlinkbased estimate of the "importance" of Web pages. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing ..."
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Cited by 167 (12 self)
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of the fact that the first eigenvalueof a Markov matrix is known to be 1 to compute the nonprincipal eigenvectorsusing successiveiterates of the Power Method. Empirically, we show that using Quadratic Extrapolation speeds up PageRank computation by 50300% on a Web graph of 80 million nodes, with minimal
Updating PageRank with Iterative Aggregation
 In Proc. of the WWW’04 Conf
, 2004
"... We present an algorithm for updating the PageRank vector [1]. Due to the scale of the web, Google only updates its famous PageRank vector on a monthly basis. However, the Web changes much more frequently. Drastically speeding the PageRank computation can lead to fresher, more accurate rankings of th ..."
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Cited by 27 (0 self)
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aggregation techniques [7, 8] to focus on the slowconverging states of the Markov chain. The most exciting feature of this algorithm is that it can be joined with other PageRank acceleration methods, such as the dangling node lumpability algorithm [6], quadratic extrapolation [4], and adaptive PageRank [3
Extrapolation Methods for Accelerating PageRank Computations
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Updating PageRank with Iterative Aggregation
 In Proc. of the WWW’04 Conf
, 2004
"... We present an algorithm for updating the PageRank vector [1]. Due to the scale of the web, Google only updates its famous PageRank vector on a monthly basis. However, the Web changes much more frequently. Drastically speeding the PageRank computation can lead to fresher, more accurate rankings of th ..."
Abstract
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aggregation techniques [7, 8] to focus on the slowconverging states of the Markov chain. The most exciting feature of this algorithm is that it can be joined with other PageRank acceleration methods, such as the dangling node lumpability algorithm [6], quadratic extrapolation [4], and adaptive PageRank [3
Computing PageRank using Power Extrapolation
, 2003
"... We present a novel technique for speeding up the computation of PageRank, a hyperlinkbased estimate of the "importance" of Web pages, based on the ideas presented in [7]. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal ..."
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Cited by 20 (2 self)
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use of known nonprincipal eigenvalues of the Web hyperlink matrix. Empirically, we show that using Power Extrapolation speeds up PageRank computation by 30% on a Web graph of 80 million nodes in realistic scenarios over the standard power method, in a way that is simple to understand and implement.
Scalable Spectral Clustering with Weighted PageRank
"... Abstract. In this paper, we propose an accelerated spectral clustering method, using a landmark selection strategy. According to the weighted PageRank algorithm, the most important nodes of the data affinity graph are selected as landmarks. The selected landmarks are provided to a landmark spectral ..."
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Abstract. In this paper, we propose an accelerated spectral clustering method, using a landmark selection strategy. According to the weighted PageRank algorithm, the most important nodes of the data affinity graph are selected as landmarks. The selected landmarks are provided to a landmark spectral
An Inner/Outer Stationary Iteration for Computing PageRank
"... We present a stationary iterative scheme for PageRank computation. The algorithm is based on a linear system formulation of the problem, uses inner/outer iterations, and amounts to a simple preconditioning technique. It is simple, can be easily implemented and parallelized, and requires minimal stor ..."
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the accelerated convergence of the algorithm compared to the power method. Key words. PageRank, power method, stationary method, inner/outer iterations, damping factor AMS subject classifications. 65F10, 65F15, 65C40 1
Chebyshev Acceleration of the GeneRank Algorithm
, 2012
"... The ranking of genes plays an important role in biomedical research. The GeneRank method of Morrison et al. [11] ranks genes based on the results of microarray experiments combined with gene expression information, for example from gene annotations. The algorithm is a variant of the well known PageR ..."
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Cited by 5 (4 self)
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The ranking of genes plays an important role in biomedical research. The GeneRank method of Morrison et al. [11] ranks genes based on the results of microarray experiments combined with gene expression information, for example from gene annotations. The algorithm is a variant of the well known PageRank
unknown title
"... Abstract—Google’s famous PageRank algorithm is widely used to determine the importance of web pages in search engines. Given the large number of web pages on the World Wide Web, efficient computation of PageRank becomes a challenging problem. We accelerated the power method for computing PageRank o ..."
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Abstract—Google’s famous PageRank algorithm is widely used to determine the importance of web pages in search engines. Given the large number of web pages on the World Wide Web, efficient computation of PageRank becomes a challenging problem. We accelerated the power method for computing PageRank
ABSTRACT
"... We present a novel algorithm for the fast computation of PageRank, a hyperlinkbased estimate of the “importance ” of Web pages. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web l ..."
Abstract
 Add to MetaCart
that the first eigenvalue of a Markov matrix is known to be 1 to compute the nonprincipal eigenvectors using successive iterates of the Power Method. Empirically, we show that using Quadratic Extrapolation speeds up PageRank computation by 50300 % on a Web graph of 80 million nodes, with minimal overhead. 1.
Results 1  10
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