We could spend the day creating this page for the ever changing Google search
algorithm, which determines a websites Google Page Rank. Rather than do that, let’s initially just give you Googles definition of what it is and then we can give some more details from there.
“PageRank reflects our (Google’s) view of the importance of web pages by considering more than 500 million variables and 2 billion terms. Pages that we believe are important pages receive a higher PageRank and are more likely to appear at the top of the search results.”
PageRank also considers the importance of each page that casts a vote, as votes from some pages are considered to have greater value, thus giving the linked page greater value. We have always taken a pragmatic approach to help improve search quality and create useful products, and our technology uses the collective intelligence of the web to determine a page’s importance”.
A PageRank results from a mathematical algorithm based on the graph created by all World Wide Web pages as nodes and hyperlinks, taking into consideration authority hubs like Wikipedia (however, Wikipedia is actually a sink rather than a hub because it uses nofollowon external links. The rank value indicates an importance of a particular page. A hyperlink to a page counts as a vote of support. The PageRank of a page is defined recursively and depends on the number and PageRank metric of all pages that link to it (“incoming links”). A page that is linked to by many pages with high PageRank receives a high rank itself. If there are no links to a web page there is no support for that page.
Numerous academic papers concerning PageRank have been published since Page and Brin’s original paper. In practice, the PageRank concept has proven to be vulnerable to manipulation, and extensive research has been devoted to identifying falsely inflated PageRank and ways to ignore links from documents with falsely inflated PageRank.
Other link-based ranking algorithms for Web pages include the HITS algorithm invented by Jon Kleinberg (used by Teoma and now Ask.com), the IBM CLEVER project, and theTrustRank algorithm.
