Google's web page ranking applied to different topological web graph structures

Meghabghab, George

doi:10.1002/asi.1124.abs

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…We are also continuing our experiments on changing graphs and in particular on the estimate of the derivative of the importance. We finally want to analyze more in-depth the impact of various specific graph patterns as done in [19] for the off-line algorithm.…”

Section: Resultsmentioning

confidence: 99%

Adaptive on-line page importance computation

Abiteboul

Preda²,

Cobena

2003

Proceedings of the Twelfth International Conference on World Wide Web - WWW '03

153

120

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The computation of page importance in a huge dynamic graph has recently attracted a lot of attention because of the web. Page importance, or page rank is defined as the fixpoint of a matrix equation. Previous algorithms compute it off-line and require the use of a lot of extra CPU as well as disk resources (e.g. to store, maintain and read the link matrix). We introduce a new algorithm OPIC that works on-line, and uses much less resources. In particular, it does not require storing the link matrix. It is on-line in that it continuously refines its estimate of page importance while the web/graph is visited. Thus it can be used to focus crawling to the most interesting pages. We prove the correctness of OPIC. We present Adaptive OPIC that also works on-line but adapts dynamically to changes of the web. A variant of this algorithm is now used by Xyleme.We report on experiments with synthetic data. In particular, we study the convergence and adaptiveness of the algorithms for various scheduling strategies for the pages to visit. We also report on experiments based on crawls of significant portions of the web.

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Section: Resultsmentioning

confidence: 99%

Adaptive on-line page importance computation

Abiteboul

Preda²,

Cobena

2003

Proceedings of the Twelfth International Conference on World Wide Web - WWW '03

153

120

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“…Haveliwala (1999) developed computing techniques to apply standard PageRank to smaller platforms. Meghabghab (2002) proposed a version based upon in and out degrees of nodes, but this did not produce improved results. Richardson and Domingos (2001) developed a combination of PageRank with content information, and probably this is what Google does already.…”

Section: Other Variations Of Pagerankmentioning

confidence: 99%

New versions of PageRank employing alternative Web document models

Thelwall¹,

Vaughan²

2004

Aslib Proceedings

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We introduce several new versions of PageRank (the link based Web page ranking algorithm), based upon an information science perspective on the concept of the Web document. Although the Web page is the typical indivisible unit of information in search engine results and most Web information retrieval algorithms, other research has suggested that aggregating pages based upon directories and domains gives promising alternatives, particularly when Web links are the object of study. The new algorithms introduced based upon these alternatives were used to rank four sets of Web pages. The ranking results were compared with human subjects' rankings. The results of the tests were somewhat inconclusive: the new approach worked well for the set that includes pages from different Web sites; however, it does not work well in ranking pages that are from the same site. It seems that the new algorithms may be effective for some tasks but not for others, especially when only low numbers of links are involved or the pages to be ranked are from the same site or directory.

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“…These make relevant Web document content accessible to users. Web crawling is also the data collection technique that supports structural and informetric analyses of the Web graph (e.g., Meghabghab, 2001). In particular, one form of analysis of the Web graph considers the inlinks and outlinks of nodes, which represent document references and citations, respectively (Cronin, 2001; Egghe & Rousseau, 1990).…”

Section: Introductionmentioning

confidence: 99%

Web‐crawling reliability

Cothey

2004

J. Am. Soc. Inf. Sci.

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Google's web page ranking applied to different topological web graph structures

Cited by 3 publications

References 6 publications

Adaptive on-line page importance computation

Adaptive on-line page importance computation

New versions of PageRank employing alternative Web document models

Web‐crawling reliability

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