Updating pagerank with iterative aggregation


To our knowledge, there are several kinds of extrapolation methods, such as quadratic extrapolation [9], two polynomial-type methods including the minimal polynomial extrapolation method (MPE) of Cabay and Jackson [24] and the reduced rank extrapolation (RRE) of Eddy [25], and the three epsilon vector extrapolation methods of Wynn [26].In recent years, many papers have discussed the application of vector extrapolation method to compute the stationary probability vector of Markov chains and web ranking problems, see [6, 8, 9, 22, 23, 27] for details.



Section 5 contains conclusion and points out directions for future research.That is to say, the current iterate can be expressed as a linear combination of some of the first eigenvectors, combined with the Power method, up to the converge of the principal eigenvector.GQE is derived in this light and can be given as follows [23].In our study, we consider the neighborhood aggregation method as described in [12–14], since it is able to result in well-balanced aggregates of approximately equal size and provide a more regular coarsening throughout the automatic coarsening process [12, 29, 33].

Our aggregation strategies are based on the problem matrix by the current iterate can be interpreted as approximations to the stationary probability vector.

Isensee and Horton considered a kind of multilevel methods for the steady state solution of continuous-time and discrete-time Markov chains in [13, 14], respectively. proposed a multilevel adaptive aggregation method for calculating the stationary probability vector of Markov matrices in [11], as shown in their context, which is a special case of the adaptive smoothed aggregation [30] and adaptive algebraic multigrid methods [31] for sparse linear systems.



Updating pagerank with iterative aggregation comments


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    May 19, 2004. On two small subsets of the web, our algorithm updates PageRank using just 25% and 14%, respectively, of the time required by the original PageRank algorithm. Our algorithm uses iterative aggregation techniques 7, 8 to focus on the slow-converging states of the Markov chain. The most exciting feature.…
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