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 was created by its founders , Larry Page and Sergey Brin who is a Ph.D. student Stanford University .
Then how does Page Rank ?
A website will be more popular if the many other sites that put links that lead to the site , with the assumption that the content / site content is more useful than the content / content of other sites . PageRank is calculated on a scale of 1-10.
Example : A website with Pagerank 9 will first sort the list of Google search than a site that has a PageRank 8 and then onwards smaller .
Many ways to use search engines to determine the quality / ranking of a web page , ranging from the use of META tags , document content , 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 ) that can be rigged with a special page designed for search engines or so-called doorway pages . With the algorithms ' PageRank' is , in every page will be inbound links ( incoming links ) and outbound link ( link keuar ) of any web page .
PageRank , have the same basic concept of link popularity , but do not just take into account " the number of" inbound and outbound links. The approach used is a page would be necessary if other pages have a link to that page . A page will also become increasingly important if other pages have a rank ( PageRank ) height refers to the page .
Approach used by PageRank , recursively process occurs where a ranking will be determined by the ranking of web pages ranking is determined by the ranking of other web pages that have links to that page . This process means a process that is repeated ( recursive ) . In the virtual world , there are millions even billions of web pages . Hence a web page ranking is determined from the overall link structure of the web pages that exist in cyberspace. A process which is very large and complex .
Want to know the page rank algorithm ?
Of the approach described in the 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 other published alogtima 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 pages that refer to pages T1 A
* C ( T1 ) is the number of outbound 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 are indexed by google )
Random surfer model is an approach which describes how exactly does a visitor in front of a web page . This means the chance or probability of a user clicking on a link is proportional to the number of links on the page . This approach is used so that the pagerank pagerank of incoming links ( inbound links ) are not distributed directly to the intended page , but divided by the number of outbound links ( outbound links) that exist on the page . It feels all too consider this fair . Because can you imagine what would happen if a page with a high ranking refers to many pages , may not be relevant PageRank technology used .
This method also has the approach that a user will not click on all the links on a web page . Therefore PageRank uses the damping factor to reduce the value of the distributed pagerank of a page to another page . Probability that a user continues mengkilk all links on a page are determined by the value of the damping factor ( d ) is a value between 0 to 1 . High damping factor value means that a user will click on a page more until he moved to another page . Once the user moves the page to the probability diimplemntasikan pagerank algorithm as constants ( 1 - d ) . By removing the variable inbound links ( incoming links ) , then it is likely a user to move to another page is ( 1 - d ) , it will make pagerank always be at its minimum .
In another pagerank algorithm , there is a value of N , which owns the total number of web pages , so the probability that a user has visited a page divided by the total number of existing pages . Sebaagai example , if a page has a pagerank 2 and a total of 100 web pages in a hundred times the visits he visited the page 2 times ( note , this is a probability ) .