**Relevance of referring pages**

As well as link text, search engines also take into account the overall information content of each referring page. Example: Suppose we are using seo to promote a car sales resource. In this case a link from a site about car repairs will have much more importance that a similar link from a site about gardening. The first link is published on a resource having a similar topic so it will be more important for search engines.

**Google PageRank – theoretical basics**

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. According to the ranking model described above: - Each page on the Net (even if there are no inbound links to it) initially has a PageRank greater than zero, although it will be very small. There is a tiny chance that a user may accidentally navigate to it. - Each page that has outbound links distributes part of its PageRank to the referenced page. The PageRank contributed to these linked-to pages is inversely proportional to the total number of links on the linked-from page – the more links it has, the lower the PageRank allocated to each linked-to page. - PageRank A “damping factor” is applied to this process so that the total distributed page rank is reduced by 15%. This is equivalent to the probability, described above, that the user will not visit any of the linked-to pages but will navigate to an unrelated website. Let us now see how this PageRank process might influence the process of ranking search results. We say “might” because the pure PageRank algorithm just described has not been used in the Google algorithm for quite a while now. We will discuss a more current and sophisticated version shortly. There is nothing difficult about the PageRank influence – after the search engine finds a number of relevant documents (using internal text criteria), they can be sorted according to the PageRank since it would be logical to suppose that a document having a larger number of high-quality inbound links contains the most valuable information. Thus, the PageRank algorithm "pushes up" those documents that are most popular outside the search engine as well.

**Google PageRank – practical use**

Currently, PageRank is not used directly in the Google algorithm. This is to be expected since pure PageRank characterizes only the number and the quality of inbound links to a site, but it completely ignores the text of links and the information content of referring pages. These factors are important in page ranking and they are taken into account in later versions of the algorithm. It is thought that the current Google ranking algorithm ranks pages according to thematic PageRank. In other words, it emphasizes the importance of links from pages with content related by similar topics or themes. The exact details of this algorithm are known only to Google developers.

You can determine the PageRank value for any web page with the help of the Google ToolBar that shows a PageRank value within the range from 0 to 10. It should be noted that the Google ToolBar does not show the exact PageRank probability value, but the PageRank range a particular site is in. Each range (from 0 to 10) is defined according to a logarithmic scale.

*Here is an example: each page has a real PageRank value known only to Google. To derive a displayed PageRank range for their ToolBar, they use a logarithmic scale as shown in this table*

*Real PR ToolBar PR*

*1-10 1*

*10-100 2*

*100-1000 3*

*1000-10.000 4*

*Etc.*

This shows that the PageRank ranges displayed on the Google ToolBar are not all equal. It is easy, for example, to increase PageRank from one to two, while it is much more difficult to increase it from six to seven.

In practice, PageRank is mainly used for two purposes:

*1. Quick check of the sites popularity.*

PageRank does not give exact information about referring pages, but it allows you to quickly and easily get a feel for the sites popularity level and to follow trends that may result from your seo work. You can use the following “Rule of thumb” measures for English language sites: PR 4-5 is typical for most sites with average popularity. PR 6 indicates a very popular site while PR 7 is almost unreachable for a regular webmaster. You should congratulate yourself if you manage to achieve it. PR 8, 9, 10 can only be achieved by the sites of large companies such as Microsoft, Google, etc. PageRank is also useful when exchanging links and in similar situations. You can compare the quality of the pages offered in the exchange with pages from your own site to decide if the exchange should be accepted.

*2. Evaluation of the competitiveness level for a search query is a vital part of seo work.*

Although PageRank is not used directly in the ranking algorithms, it allows you to indirectly evaluate relative site competitiveness for a particular query. For example, if the search engine displays sites with PageRank 6-7 in the top search results, a site with PageRank 4 is not likely to get to the top of the results list using the same search query. It is important to recognize that the PageRank values displayed on the Google ToolBar are recalculated only occasionally (every few months) so the Google ToolBar displays somewhat outdated information. This means that the Google search engine tracks changes in inbound links much faster than these changes are reflected on the Google ToolBar.

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