PageRank is a patented algorithm that serves to determine which website 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.
A site will be more popular if more and more other sites that put links that lead to its 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 at first listing in the Google search list rather than a site that has a Pagerank 8 and then onwards smaller. (I kok less intercourse agreed to it? his keq sih gak gitu deh ...)
Many ways to use search engines to determine the quality / ranking of a web page, from the use of META Tags, the contents of the document, the emphasis on content and many other techniques or 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 a special page designed for search engines or so-called doorway pages. With the algorithm 'PageRank' is, in every page will be taken into account inbound links (incoming link) and outbound links (links keuar) of each web page.
PageRank, has the same basic concept with link popularity, but not only take into account "the number of" inbound and outbound links. The approach used is an important page will bet if other pages have a link to that page. A page will also become increasingly important if other pages have a rank (pagerank) 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 pages is determined by the ranking rangkingnya other web pages that have a link to that page. This process means a process that is repeated (recursively). 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 in cyberspace. A process very large and complex.
Of the approach already described in this article the concept of pagerank, Lawrence Page and Sergey Brin made pagerank algorithm as below:
Initial algorithm PR (A) = (1-d) + d ((PR (T1) / C (T1)) + ... + (PR (Tn) / C (Tn)))
One of the other alogtima published PR (A) = (1-d) / N + d ((PR (T1) / C (T1)) + ... + (PR (Tn) / C (Tn)))
* PR (A) is the Pagerank page A
* PR (T1) is the Pagerank T1 page that refers to page A
* C (T1) is the number of links (outbound links) on page T1
* D is a damping factor which can be between 0 and 1.
* N is the total number of web pages (which is indexed by google)
From the above algorithms can be seen that the pagerank is determined for each of your pages is not the whole web site. Pagerank of a page is determined from pagerank pages that also refer to him through the process of determining pagerank in the same manner, so this process will be repeated until the appropriate results found. But the pagerank page A is not directly given to the intended page, but previously divided by the number of links on the page T1 (outbound links), and the pagerank will be shared equally among all the links that exist on the page. So it is with every other page "Mr." which refers to the page "A". After all pagerank gained from other pages that refer to the page "A" added, the value is then multiplied by the damping factor value between 0 to 1. This is done to avoid the overall value of T distributed pagerank 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 the opportunity or the probability of a user 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 the incoming link (inbound links) are not directly distributed to the targeted page, but divided by the number of links (outbound links) that exist on the page. It was all too thought it was fair. Because can you imagine what would happen if a page with a high ranking refers to many pages, the technology may not be relevant pagerank used.
This method also has an approach that a user will not click on any link on a web page. Therefore pagerank using the 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) the value between 0 to 1. Value of high damping factor means that a user will click on more of a page until he moved to another page. After the user moves the page into diimplemntasikan probability pagerank algorithm as a constant (1-d). By issuing a variable inbound links (links in), then the possibility of a user to move to another page is (1-d), this will make pagerank always be at the minimum value.
In other pagerank algorithm, there are values of N the total merupkan 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 visited that page as much as 2 times (note, this is the probability).
Well ..., frankly binun own sayah read this, reply mudeng sayah ndak mah-teknikan techniques ato keq gini SEO techniques, kalo pakek seneng sayah more korengan hand technique means kalo updated every day this hand itches it all, nah kalo itch carded can keep this korengan ... nah this technique during my nyang apply. So sing important update
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