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9. Additional Factors Influencing PageRank (continued)

Different Evaluation of Links by Page Specific Criteria

Besides the unequal evaluation of links within a document, Lawrence Page mentions the possibility of evaluating links according to criteria which are based upon the linking page. At first glance, this does not seem necessary since it is the main principle of

 
Table of Contents
 

Survey of Google’s PageRank
1. Introduction
2. The PageRank Algorithm
3. Page Rank Implementation
4. Effect Of Inbound Links
5. Effect of Outbound Links
6. Effect of Number of Pages
7. PageRank Redistribution
8. The Yahoo Bonus
9. Additional Factors
10. Theme-Based Page Rank
11. PR0 Penalty

 

PageRank to rank pages the higher, the more high ranking pages link to them. But, at the time of their scientific work on PageRank, Page and Brin have already recognized that their algorithm is vulnerable to artificial inflation of PageRank.

An artificial influence on PageRank might be exerted by webmasters who generate a multitude of web pages whose links distribute PageRank in a way that single pages within that system receive a special importance. Those pages can have a high PageRank without being linked to from other pages with high PageRank. So, not only the concept of PageRank is undermined, but also the search engine's index is spammed with an innumerable amount of web pages which were solely created to influence PageRank.

In his patent specifications for PageRank, Lawrence Page presents the evaluation of links by the distance between pages as a means to avoid the artificial inflation of PageRank, because the bigger the distance between two pages, the less likely has one webmaster control over both. A criterium for the distance between two pages may be if they are on the same domain or not. In this way, internal links would be weighted less than external links. In the end, any general measure of the distance between links can be used to determine such a weighting. This comprehends if pages are on the same server or not and also the geographical distance between servers.

As another indicator for the importance of a document, Lawrence Page mentions the up-to-dateness of the documents which link to it. This argument considers that the information on a page is less likely outdated, the more pages which have been modified recently link to it. In contrast, the original PageRank concept, just like any method of measuring link popularity, favours older documents which gained their inbound links in the course of their existence and have at a higher probability been modified less recently than new documents. Basically, recently modified documents may be given a higher evaluation by weighting the factor (1-d). In this way, both those recently modified documents and the pages they link to receive a higher PageRank. But, if a page has been modified recently, is not necessarily an indicator for the importance of the information presented on it. So, as suggested by Lawrence Page, it is advisable not to favour recently modified pages but only their outbound links.

Finally, Page mentions the importance of the web location of a page as an indicator of the importance of its outbound links. As an example for an important web location he names the root page of a domain, but, in the end, Google could exert influence on PageRank absolutely arbitrarily.

To implement the evaluation of the linking page into PageRank, the evaluation factor of the modified algorithm must consist of several single factors. For a link that points from page Ti to page A, it can be given as follows:

L(Ti,A) = K(Ti,A) × K1(Ti) × ... × Km(Ti)

where K(Ti,A) is the above presented weighting of a single link within a page by its visibility or position. Additionally, an evaluation of page Ti by m criteria which are represented by the factors Kj(Ti) takes place.

To implement the evaluation of the linking pages, not only the algorithm but also the proceedings of PageRank calculation have to be modified. This shall be illustrated by an example.

We take a look at a web consisting of three pages A, B and C, whereby page A links to the pages B and C, page B links to page C and page C links to page A. The outbound links of one page are evaluated equally, so there is no weighting by visibilty or position. But now, the pages are evaluated by one criterium. In this way, an inbound link from page C shall be considered four times as important as an inbound link from one of the other pages. After weighting by the
number of pages, we get the following evaluation factors:

K(A) = 0.5
K(B) = 0.5
K(C) = 2

At a damping factor d of 0.5, the equations for the computation of the PageRank values are given by

PR(A) = 0.5 + 0.5 × 2 PR(C)
PR(B) = 0.5 + 0.5 × 0.5 × 0.5 PR(A)
PR(C) = 0.5 + 0.5 (0.5 PR(B) + 0.5 × 0.5 PR(A))

Solving the equations gives us the follwing PageRank values:

PR(A) = 4/3
PR(B) = 2/3
PR(C) = 5/6

At the current modifications of the PageRank algorithm, the accumulated PageRank of all pages no longer equals the number of pages. The reason therefore is that the weighting of the page evaluation by the number of pages was not appropriate. To determine the proper weighting, the web's linking structure would have to be anticipated, which is not possible in case of the actual WWW. Therefore, the PageRank calculated by an evaluation of linking pages has to be normalized if there shall not be any unfounded effects on the general ranking of pages by Google. Within the iterative calculation, a normalization would have to take place after each iteration to minimize unintentional distortions.

In the case of a small web, the evaluation of pages often causes severe distortions. In the case of the actual WWW, these distortions should normally equalise by the number of pages. Indeed, it is to be expected that the evaluation of the distance between pages will cause distortions on PageRank, since pages with many inbound links surely tend to be linked to from different geographical regions. But such effects can be anticipated by experience from previous calculation periods, so that a normalisation would only have to be marginal.

In either case, implementing additional factors in PageRank is possible. Indeed, the computation of PageRank values would take more time.

Next Article Segment
10. Theme-Based PageRank

 

This article reproduced with permission of eFactory.
© 2002 eFactory Internet-Agentur KG Online-Marketing - written by Markus Sobek
PageRank and Google are trademarks of Google Inc., Mountain ViewCA, USA.
PageRank is protected by US Patent 6,285,999.

 
 

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