Deeper Inside PageRank

Langville, Amy N.; Meyer, Carl D.

doi:10.1080/15427951.2004.10129091

Cited by 730 publications

(514 citation statements)

References 76 publications

Supporting

Mentioning

506

Contrasting

Unclassified

Order By: Relevance

“…The coefficient matrix (I−αH) of the linear system has many nice properties, which were proven in [12]. Some that are relevant for this paper are as follows:…”

Section: A Linear System Formulation For Exploiting Dangling Nodesmentioning

confidence: 99%

A Reordering for the PageRank Problem

Langville¹,

Meyer²

2006

SIAM J. Sci. Comput.

View full text Add to dashboard Cite

Abstract. We describe a reordering particularly suited to the PageRank problem, which reduces the computation of the PageRank vector to that of solving a much smaller system and then using forward substitution to get the full solution vector. We compare the theoretical rates of convergence of the original PageRank algorithm to that of the new reordered PageRank algorithm, showing that the new algorithm can do no worse than the original algorithm. We present results of an experimental comparison on five datasets, which demonstrate that the reordered PageRank algorithm can provide a speedup of as much as a factor of 6. We also note potential additional benefits that result from the proposed reordering.

show abstract

“…The coefficient matrix (I−αH) of the linear system has many nice properties, which were proven in [12]. Some that are relevant for this paper are as follows:…”

Section: A Linear System Formulation For Exploiting Dangling Nodesmentioning

confidence: 99%

A Reordering for the PageRank Problem

Langville¹,

Meyer²

2006

SIAM J. Sci. Comput.

View full text Add to dashboard Cite

show abstract

“…Because pages only link to a few others (the link matrix is sparse), this results in much lower memory requirements of the link structure, in the magnitude of | L | · n −1 (n = average outdegree). Of course, compression techniques [14] or disk-based "swapping" [9,5] can improve the space requirements even further. But with the permanent growth of the web, even such techniques will soon hit memory limits of a single computer, or unacceptably slow down the computation process.…”

Section: Web Graph Representationmentioning

confidence: 99%

Efficient Parallel Computation of PageRank

Kohlschütter

Chirita

Nejdl

2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. PageRank inherently is massively parallelizable and distributable, as a result of web's strict host-based link locality. In this paper we show that the Gauß-Seidel iterative method for solving linear systems can be successfully applied in such a parallel ranking scenario in order to improve convergence. By introducing a two-dimensional web model and by adapting the PageRank to this environment, we present and evaluate efficient methods to compute the exact rank vector even for large-scale web graphs in only a few minutes and iteration steps, with intrinsic support for incremental web crawling, and without the need for page sorting/reordering or for sharing global information.

show abstract

“…Here, the convex combination of P with the perturbation matrix E = ee T n ensuresP to be irreductible by definition 1 [7]. The intuition behind this approach is to model the behaviour of a "random surfer" that with probability (1−α) gets bored and makes a jump to an arbitrary site.…”

Section: Preliminariesmentioning

confidence: 99%

Classifier Combination Using Random Walks on the Space of Concepts

Sánchez

Redolfi

2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Abstract. We propose a novel approach for the combination of classifiers based on two commonly adopted strategies in multiclass classification: one-vs-all and one-vs-one. The method relies on establishing the relevance of nodes in a graph defined in the space of concepts. Following a similar approach as in the ranking of websites, the relative strength of the nodes is given by the stationary distribution of a Markov chain defined on that graph. The proposed approach do not requires the base classifiers to provide calibrated probabilities. Experiments on the challenging problem of multiclass image classification show the potentiality of our approach.

show abstract

Deeper Inside PageRank

Cited by 730 publications

References 76 publications

A Reordering for the PageRank Problem

A Reordering for the PageRank Problem

Efficient Parallel Computation of PageRank

Classifier Combination Using Random Walks on the Space of Concepts

Contact Info

Product

Resources

About