System developed by the founders of Google, which assigns a rating to a web page based on external links to this page and the nature of the sites where these links are present. PageRank (TM) is an element strongly influence the position of a page in Google results.
PageRank (English word ‘ranking of a page “) or PR refers to the popularity of a Web page used by the Google search engine rankings for its search results. PageRank also refers to Larry Page Google co-founder and inventor of the principle.
Google assigns a rating of popularity for each Web page. This note is made on the basis of external links (link popularity) pointing to it and links it makes to itself (internal links). In this calculation is an integrated PageRank algorithms to rate stable at each page.
The real PageRank of a page is known only to Google, but users can obtain an approximation of the ranking of the page by looking at the area of the awesome Google PageRank Thing, which shows its base of information on a scale of 0 to 20 (Scale logarithmic).
PageRank was pregnant a factor in the ranking of search results from Google. This is less true today.
Referrers have long created massive link exchanges.
See also our definition on TrustRank.
Improve the “relevance” of the results is a major technological goal for most search engines. The problem is that “relevance” is a purely subjective concept, it is difficult to handle with purely mathematical algorithms.
The search engine Google, notammment, seeking for years a solution to this problem. The first tracks in this direction have been launched by Larry Page, in a publication published while still a student at Stanford University.
Stanford retains from the days of Page and Brin, the founders of Google, a research department dedicated to Pagerank. For two years, a new team of researchers has paved the way for solutions to calculate personalized PageRank, and thus to refine, with a remarkable economy of means, the engine results pages in a given context.
This work is little known outside specialist circles and are not yet used by all engines. But There’s a good bet that the first engines theme, built around this type of algorithm, should not delay to come out: some researchers from Stanford in June 2003 formed the company Kaltix, acquired by Google in late September 2003 …
The advantages and limitations of Pagerank
The PageRank algorithm: brute force computing in the service of the relevance of results
Google has built its success around an algorithm for storing the index of the engine, with each page, a “value” indicative of the “importance” of pages on the web. Reference is made to excellent articles by Dan Hetzel to understand the principles [1] This value assumes that the intuitive importance of a page depends on the number of incoming links pointing to this page, but also the importance of pages from which these links.
Calculate a PageRank for a page request then an iterative calculation, since a page of a build pagerank in turn alters the pagerank of all pages that point to a link of that page and all pages linked to these pages. Any modification of the PageRank of a page to “spread” so over the links from page to page …But if any of the linked pages as well, regardless of the number of links that separates it from the page that was calculated pagerank, contains a reciprocal link to the homepage, it also changes the pagerank of the homepage .
The iterative algorithm for calculating PageRank converges after a number of iterations to a fixed value. The problem is that this “convergence” of the sequence of results is not ensured in theory (but it is in practice given the actual structure of the web) and the “speed of convergence” (the number of iterations to reach a specific value close to the limit) is very variable according to areas of the web.
The computation of PageRank takes time and computing power phénomale
The computing power required to calculate the PageRank for an index size of Google is quite amazing. But fortunately, they are essentially very simple matrix calculations, which are manageable with an architecture based on a large number of machines each calculating the values for a small area of the matrix huge web.
According to some “leakage” from the Googleplex, the computation time required to calculate the PageRank of all the Web pages indexed took up a good week … (The method used has changed in spring 2003, will be seen below.)
By cons, once this calculation, the pagerank is stored in the index with pages and is available to refine the calculation of positions on search results pages, which also depends on other more conventional criteria, but specific each page, such as keyword density, for example.
The architecture of an engine based on PageRank
The method devised by Page and Brin has two virtues: the calculations more complex and longer are offline. The engine has only very simple calculations to be made to build search results pages on the fly.
Then, taking into account only the “number of links” as a criterion allows complete mathematical model of calculating the “value of a page.” On the theoretical side, the PageRank is a calculation that measures the consequences of the behavior of a user randomly teleported from page to page at random links that connect them.
The limits of PageRank
On the theoretical side, the assumption of Brin and Page that the value of pages depends on the number of incoming links was criticized in 1999 by researchers at the IBM Almaden laboratory. These criticisms focused on two points:
this assumption ignores the real structure of the web, which is not uniformly connected by hypertext links.
PageRank provides no semantic information, and is likely to give importance to pages that do not
One can verify in practice the accuracy of these remarks by consulting the results pages returned by Google.
