The Google company was the first
company to patent the system of taking into account inbound links. The
algorithm was named PageRank. In this section, we will describe this algorithm
and how it can influence search result ranking.
PageRank is estimated separately for each web page and is determined by the PageRank (citation) of other pages referring to it. It is a kind of “virtuous circle.” The main task is to find the criterion that determines page importance. In the case of PageRank, it is the possible frequency of visits to a page.
I shall now describe how user’s behavior when following links to surf the network is modeled. It is assumed that the user starts viewing sites from some random page. Then he or she follows links to other web resources. There is always a possibility that the user may leave a site without following any outbound link and start viewing documents from a random page. The PageRank algorithm estimates the probability of this event as 0.15 at each step. The probability that our user continues surfing by following one of the links available on the current page is therefore 0.85, assuming that all links are equal in this case. If he or she continues surfing indefinitely, popular pages will be visited many more times than the less popular pages.
The PageRank of a specified web page is thus defined as the probability that a user may visit the web page. It follows that, the sum of probabilities for all existing web pages is exactly one because the user is assumed to be visiting at least one Internet page at any given moment.
Since it is not always convenient to work with these probabilities the PageRank can be mathematically transformed into a more easily understood number for viewing. For instance, we are used to seeing a PageRank number between zero and ten on the Google Toolbar.
PageRank is estimated separately for each web page and is determined by the PageRank (citation) of other pages referring to it. It is a kind of “virtuous circle.” The main task is to find the criterion that determines page importance. In the case of PageRank, it is the possible frequency of visits to a page.
I shall now describe how user’s behavior when following links to surf the network is modeled. It is assumed that the user starts viewing sites from some random page. Then he or she follows links to other web resources. There is always a possibility that the user may leave a site without following any outbound link and start viewing documents from a random page. The PageRank algorithm estimates the probability of this event as 0.15 at each step. The probability that our user continues surfing by following one of the links available on the current page is therefore 0.85, assuming that all links are equal in this case. If he or she continues surfing indefinitely, popular pages will be visited many more times than the less popular pages.
The PageRank of a specified web page is thus defined as the probability that a user may visit the web page. It follows that, the sum of probabilities for all existing web pages is exactly one because the user is assumed to be visiting at least one Internet page at any given moment.
Since it is not always convenient to work with these probabilities the PageRank can be mathematically transformed into a more easily understood number for viewing. For instance, we are used to seeing a PageRank number between zero and ten on the Google Toolbar.
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