Search Engine Optimization Programs | SEO Tutorial Program

Google PageRank – Theoretical Basics

PageRank, first implemented by the search engine giant Google, it is a numerical value given to a website, which signifies the importance of a web page. PageRank is an estimation that is made separately for each web page where the number of other pages that refer to the site are taken into consideration.

The PageRank has a lot of influence on a web page. Usually, when the search engine finds numerous documents with internal text criteria, these documents are sorted depending on its PageRank. This is because the document having the maximum high quality links to it is the document having maximum information of value. So with this, documents that are found outside a search engine are also pushed up with the help of a PageRank algorithm.

Basically, a PageRank of a page is the probability of a user visiting that web page. Of course, the probability of all web pages is usually ‘1’, as a surfer is always visiting a web page at a specific time. So when surfing indefinitely, the user usually starts from a random page and follows links to other sites. Then the user leaves the site again to visit another random page; and so when surfing indefinitely, it is the most popular web pages that are visited more frequently than the pages that are less common.

So this means that all pages on the internet have a PageRank that is slightly greater than zero, as there is always a chance of a user accidentally visiting it. Pages having outbound links pass on a part of this PageRank to the page linked to it. The PageRank contribution by the web page to the linked pages is found to be inversely proportional to the sum of the number of links the main page has. Sometimes a ‘damping factor’ is used on this process of PageRank distribution so that the total of the page rank that is distributed experiences a reduction of 15%.

When taking PageRank theoretically, it basically interprets a website to be multigraphs having some pages on its tops. With these multigraphs, it is basically possible to describe and build up in self-learning systems. It is the spider that Google uses that tends to explore a site, wherein it traces all links existing between pages. With this, a matrix of links is formed that has information on the topology that is found in a site.

There is a PageRank formula that takes into consideration, and then computes the approximate value of a web page. This is done by paying attention to the number and types of links found between pages. Then there is also the Topic sensitive PageRank where an attempt is made to have the system of finding the PageRank more accurate. Here, the Topic Sensitive Page Rank (TSPR) calculates the different PageRanks for a specific topic instead of a single PageRank. Of course, the topic that is used for calculations depends on the content of each web page. With this idea, it is possible to create numerous PageRanks for a web document with the help of pre-calculated vectors and topic based vectors. In the case of Topic Sensitive Page Rank, links from irrelevant sites are given less weight in the calculation of the PageRank. Whereas, pages having less incoming links, but from related sites are given more importance. With this, the site ends up with a higher TSPR, despite a lower PageRank.

Search Engine Optimization Programs

SEO Tutorial Progaram

info@seotutorialprogram.com

Scottsdale Arizona

Increase Website Traffic   Internet Marketing  SEO Program  SEO BOOK  Affiliate Program

Personal Trainer