How Google Page Rank Works

How Google Page Rank Works

PageRank is a patented algorithm that has a working web site determine which is more important / popular. PageRank is one of the main features of the Google search engine and was created by the founders, Larry Page and Sergey Brin is a Ph.D. student Stanford University.How it works
A site will be more popular if more laying other sites that link that leads to the site, with the assumption that the content / site content is more useful from the content / content of other sites. PageRank is calculated with a scale of 1-10.Example: A site that will have a PageRank of 9 Sort list first in a Google search of the site have a PageRank of 8 and then the next smaller.

Concept
How many search engines use in determining the quality / ranking of a web page, from the use 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 lack of other technology (Meta Keywords, Meta Description), which can dicurangi with a special page designed for search engines is called regular or doorway pages. With the algorithm 'PageRank' is, in each page will be inbound link (incoming link) and outbound links (links keuar) from each web page.PageRank, have the same basic concept of link popularity, but it does not only consider the "number of" inbound and outbound links. The approach used is a page diangap will be important if other pages have a link to the page. A page will also become increasingly important if other pages have a rank (PageRank) to high 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 that rangkingnya is determined by the ranking of web pages that have links to the page. This process means a process of repeated (rekursif). In the virtual world, there are millions and even billions of web pages. Thus a web page ranking is determined from the link structure of the entire web page in the virtual world. A process which is very large and complex.

Algorithm
From the approach that has been described in the article the concept of PageRank, Lawrence Page and Sergey Brin created the PageRank algorithm as below:

Initial algorithm
PR (A) = (1-d) + d ((PR (T1) / C (T1)) + ... + (PR (Tn) / C (Tn)))
One of the other published alogtima
PR (A) = (1-d) / N + d ((PR (T1) / C (T1)) + ... + (PR (Tn) / C (Tn)))
PR (A) A page is the PageRank
PR (T1) is the PageRank of page T1 refers to page A
C (T1) is the number of outbound links (outbound links) on page T1
d is the proximity factor can be between 0 and 1.
N is the overall number of web pages (which terindex by google)

Of our algorithm above can be seen that the PageRank for every page of your site is not the entire web. PageRank of a page's PageRank is the page that refers to him, which is also undergoing the process of determining the PageRank in the same way, so this process will be repeated until the exact results found. Akan A page's PageRank but not given directly to the page that dituju, but the previously divided by the number of links in the page T1 (outbound link), and PageRank will be divided evenly to each link in the page. Likewise with every other page "Tn" which refers to the "A". After all that the PageRank of pages that refer to the "A" note, the value is then multiplied by the proximity factor value between 0 and 1. This is not to the overall value of Q distributed PageRank page to page A.

Random surfer model
Random surfer model is the approach that is indeed how a visitor in front of a web page. This means the opportunity or the probability of a user clicking a link comparable to the number of links that are on the page. This approach is used so that the PageRank PageRank of incoming links (inbound links) are not distributed directly to the page dituju, but divided by the number of outbound links (outbound links), which is on the page. It also considers all of this fair. Because can you imagine what happens when a page with a high-ranking refers to many pages, the PageRank technology may not be relevant to use.This method also has the approach that a user will not click on any link that is on a web page. Therefore, PageRank uses proximity factor for mereduksi value of PageRank is distributed in a page to another page. The probability of a user mengkilk hold all the links on a page is determined by the value of proximity factor (d) values between 0 and 1. Proximity factor value means that a high user will click a lot more pages until he moved to another page. After the user switch the page to the probability diimplemntasikan in the PageRank algorithm as a constant (1-d). Removing the variable with the inbound links (incoming links), the possibility of a user to move to another page is (1-d), this will make PageRank always be at the minimum value.PageRank algorithm in the other, there is a value N merupkan the overall number of web pages, so a user has a probability to visit a page divided by the total number of pages available. Sebaagai example, if a page has a PageRank 2 and total web page 100 in the hundred times he visits the page that is 2 times (note, this is a probability).