Microsoft Unveils BrowseRank, Google Feels a Draft - All About PageRank
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As a disclaimer, let me just point out that PageRank is not the only way Google determines site importance. “It's important to keep in mind that PageRank is just one of more than 200 signals we use to determine the ranking of a website,” Google said. “Search remains at the core of everything Google does, and we are always working to improve it.” Having said that, Google still relies heavily upon it, so let's begin the analysis.
PageRank is a trademark of Google and is one of several link analysis algorithms, which use the link graph of the web to determine page importance. HITS is another popular link analysis algorithm. Basically, PageRank calculates the number of links to a specific page and examines the importance of each of those pages. Here is Google's description:
In essence, Google interprets a link from page A to page B as a vote, by page A, for page B. But, Google looks at more than the sheer volume of votes, or links a page receives; it also analyzes the page that casts the vote. Votes cast by pages that are themselves “important” weigh more heavily and help to make other pages “important.”
PageRank relies on other factors, such as the relevance of keywords on the page and the number of visits to the page obtained from the Google Toolbar, that have been known to be prime targets for manipulation. Because of this, Google is not divulging the details of several other factors that influence PageRank.
The algorithm itself assesses the probability that a person who is randomly clicking on links will end up at a specific page. This calculation is done recursively in order to obtain the most accurate final value. The initial approximation would be evenly divided among the number of pages being examined (the total must equal 1). So for example, if there are 10 web pages, each would have an initial PageRank of 0.1. When you add links to the equation, things start to get hairy.
First of all, the PageRank of one page (X) is determined by the pages linking to it. For each page linking to X: the initial PageRank is divided by the number of outbound links it has (in our case, it would be 0.1 divided by outbound links). This value is then added to values of all the other pages linking to X. This will give you the PageRank of X. Then we have to adjust for the damping factor, which is the probability that a person randomly clicking on links will continue to do so, as well as pages that have no links to other pages.
If you would like to further explore the intricacies of PageRank, then I urge you to check out its Wikipedia page. There are plenty of links and references to help you along your educational journey. That being said, I hope you like math.
Next: BrowseRank to the Rescue >>
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