PageRank is a patented algorithm that has a functioning web site to determine which is more important / popular. PageRank is one of the main features of the Google search engine and created by its founder, Larry Page and Sergey Brin, who is a Ph.D. student Stanford University.

How it works
A site will be increasingly popular as an increasing number of other sites that put links that lead to his Web site, with the assumption that the content / site content is more useful than the content / content of other sites. PageRank is calculated with a scale of 1-10.

Example: A site that has a Pagerank 9 will be sorted first in the list of Google search than the sites that have a Pagerank 8 and then onwards smaller.

Many ways to use search engines to determine the quality / ranking of a web page, from the use of META Tags, the contents of a document, the emphasis on content and many other techniques or a combination of techniques that may be used. Link popularity, a technology developed to improve the shortcomings of other technologies (Meta Keywords, Meta Description), which can be rigged with special pages designed for search engines or so-called doorway pages. With the algorithms 'PageRank' is, in every page will be counted inbound links (incoming links), and outbound links (links keuar) of any web pages.

PageRank, has the same basic concept of link popularity, but not only considers the "amount" of inbound and outbound links. The approach used is an important supposed would page if another page has a link to that page. A page will also become increasingly important if other pages ranking (PageRank) of high refers to the page.

With the approach used by the PageRank, the process occurs recursively, where a ranking will be determined by the ranking of web page ranking is determined by the rankings of other web pages that have links to those pages. This process means a process that is repeated (recursive). In the virtual world, there are millions and even billions of web pages. Therefore a web page ranking is determined from the overall link structure of web pages that exist in cyberspace. A process that is very large and complex.

Algorithm
From an approach that has been described in articles PageRank concept, Lawrence Page and Sergey Brin makes PageRank algorithm as below:


initial algorithm PR(A) = (1-d) + d ( ( PR(T1) / C(T1) ) + … + ( PR(Tn) / C(Tn) ) )
One other published alogtima PR(A) = (1-d) / N + d ( ( PR(T1) / C(T1) ) + … + ( PR(Tn) / C(Tn) ) )

Of the algorithms above can be seen that the PageRank for each page is determined you are not a whole web site. Pagerank PageRank of a page is determined from the page that refers to him who is also undergoing the process of determining pagerank in the same way, so this process will be repeated until the correct results were found. But the pagerank page A is not directly given to the target pages, but the previously divided by the number of links that existed at T1 pages (outbound links), and the PageRank will be divided equally to every link on that page. Likewise with every other page "Mr." which refers to the page "A". Having obtained all the PageRank of other pages that refer to the page "A" are added, the value is then multiplied by the damping factor is a value between 0 to 1. This is done to avoid a total sum distributed pagerank T page to page A.

Random surfer model
Random surfer model is an approach that illustrates how real that is a visitor in front of a web page. This means that the chances or probability that a user actually clicks on a link is proportional to the number of links that exist on the page. This approach is used so that the pagerank pagerank of incoming links (inbound links) are not directly distributed to the target page, but divided by the number of outgoing links (outbound links), which exist on the page. It was all too consider this fair. Because can you imagine what would happen if a high ranking pages by referring to the many pages, it may not be relevant PageRank technology used.

This method also has the approach that a user will not click on any link on a webpage. Therefore, PageRank uses a damping factor to reduce the value of the distributed PageRank of a page to another page. The probability of a user continues mengkilk all existing links on a page is determined by the value of damping factor (d) that a value between 0 to 1. Value of high damping factor means that a user will click on a page more until he moved to another page. When users switch pages, then the probability diimplemntasikan into PageRank algorithm as a constant (1-d). By issuing variable inbound links (incoming links), then the likelihood of a user to move to another page is (1-d), this will make the pagerank always be at a minimum value.

In another algorithm, PageRank, there are values of N merupkan total web pages, so a user has a probability of visiting a page divided by the total number of pages available. Sebaagai example, if a page has a PageRank 2 and a total of 100 web pages in one hundred times the requests he had visited that page as much as two times (note, this is a probability).