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Page Rank and its impact

Trade marked and patented, the PageRank system is a Google algorithm developed by the Stanford University and it helps determine the importance of web pages by employing a numerical system in the allocation of appropriate search engine ranking for respective pages. The Page Rank link analysis system is democratic in its functions, it interprets link connections as ballots in favor of the receiver, and it also pays close attention to those pages with the relevant voting links. In the main a page receiving links derives extra Page Rank weight on the account of the number and Page rank metrics of the total sum of pages pointing to it, high value connections translates to high Page Rank. The importance of all web pages is worked out against algorithm numeric weighting of 0-10 (logarithmic scale), and in addition factors such as search words and phrases available on the respective page and the frequency of visits as witnessed by the Google Toolbar - this assists in placing pages in their rightful rankings.

It is also well known that Google employs up to 200 other confidential criteria into the Page Rank allocation system, and mainly these are aimed at thwarting unscrupulous attempts to game search engines through illegal search engine optimization tactics such as spamdexing and spoofing. Although search engines such as Google invest so much time and effort into preventing manipulative tactics, it has been reported that even the Page Rank system is not immune to manipulation. There are other link based algorithms in use such as the HITS algorithm utilized by Teoma, Ask.com, the IBM CLEVER project and the Trust Rank algorithm.

The Page Rank concept was partly inspired by Eugene Garfield's citation analysis created in the 1950's at the University of Pennsylvania, it cited the fact that through link channeling virtual communities of web pages come to life. Computation of Page Rank use iterations to adjust approximate values of Page rank as a means to attain the closest theoretical true value, and a probability of 0,5 based on a numerical value of 0 and 1 translates to 50% chance of a web surfer clicking on a random link will land at the respective page showing a Page Rank of 0.5. The more the volume of documents handled by Google the lower the approximation of Page Rank in relation to each and every document and this normally happens as Google recalculates Page Rank tallies after crawling the web and restructuring the index.

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