With the growth of the Internet, the field of Information Retrieval (IR) has gained increasing importance. Quick, easy, and accurate information access is the deciding factor between successful search companies and their rivals. Likewise, the manipulation of IR systems for ulterior motives, known as adversarial IR, is just as important, as it can turn the successes of a search strategy against ...

In this paper we present new ideas to accelerate the computation of the eigenvector of the transition matrix associated to the PageRank algorithm. New ideas are based on the decomposition of the matrix-vector product that can be seen as a fluid diffusion model, associated to new algebraic equations. We show through experimentations on synthetic data and on real data-sets how much this approach ...

We apply the Google PageRank algorithm to assess the relative importance of all publications in the Physical Review family of journals from 1893 to 2003. While the Google number and the number of citations for each publication are positively correlated, outliers from this linear relation identify some exceptional papers or “gems” that are universally familiar to physicists. © 2006 Published by ...

The PageRank algorithm plays an important role in modern search engine technology. It involves using the classical power method to compute the principle eigenvector of the Google matrix representing the web link graph. However, when the largest eigenvalue is not well separated from the second one, the power method may perform poorly. This happens when the damping factor is sufficiently close to...

In this paper we present some notes of the PageRank algorithm, including its L1 condition number and some observation of the numerical tests of two variant algorithms which are based on the extrapolation method. 2005 Elsevier Inc. All rights reserved.

In this paper we present some notes of the PageRank algorithm, including its L1 condition number and some observation of the numerical tests of two variant algorithms which are based on the extrapolation method. 2005 Elsevier Inc. All rights reserved.

The PageRank is used by search engines to reflect the popularity and importance of a page based on its reference ranking. Since the web changes very fast, the PageRank has to be regularly updated. Such updates is an challenging task due to the huge size of the World Wide Web. Consequently, the analysis of the PageRank has become a hot topic with vast literature ranging from the original paper b...

The iterative aggregation/disaggregation (IAD) method is an improvement of the PageRank algorithm used by the search engine Google to compute stationary probabilities of very large Markov chains. In this paper the convergence, in exact arithmetic, of the IAD method is analyzed. The IAD method is expressed as the power method preconditioned by an incomplete LU factorization. This leads to a simp...

We introduce a novel bookmark-coloring algorithm (BCA) that computes authority weights over the web pages utilizing the web hyperlink structure. The computed vector (BCV) is similar to the PageRank vector defined for a page-specific teleportation. Meanwhile, BCA is very fast, and BCV is sparse. BCA also has important algebraic properties. If several BCVs corresponding to a set of pages (called ...

Existing coupling metrics only use the number of methods invocations, and does not consider the weight of the methods. Thus, they cannot measure coupling metrics accurately. In this paper, we measure the weight of methods using PageRank algorithm, and propose a new approach to improve coupling metrics using the weight. We validate the proposed approach by applying them to several open source pr...