**3.1 Why inbound links to sites are taken into account**

As you can see from the previous section, many factors influencing the ranking process are under the control of webmasters. If these were the only factors then it would be impossible for search engines to distinguish between a genuine high-quality document and a page created specifically to achieve high search ranking but containing no useful information. For this reason, an analysis of inbound links to the page being evaluated is one of the key factors in page ranking. This is the only factor that is not controlled by the site owner.

It makes sense to assume that interesting sites will have more inbound links. This is because owners of other sites on the Internet will tend to have published links to a site if they think it is a worthwhile resource. The search engine will use this inbound link criterion in its evaluation of document significance.

Therefore, two main factors influence how pages are stored by the search engine and sorted for display in search results:

– Relevance, as described in the previous section on internal ranking factors.

– Number and quality of inbound links, also known as *link citation*, *link popularity* or *citation index*. This will be described in the next section.

**3.2 Link importance (citation index, link popularity)**

You can easily see that simply counting the number of inbound links does not give us enough information to evaluate a site. It is obvious that a link from www.microsoft.com should mean much more than a link from some homepage like www.hostingcompany.com/~myhomepage.html. You have to take into account *link importance* as well as number of links.

Search engines use the notion of citation index to evaluate the number and quality of inbound links to a site. Citation index is a numeric estimate of the popularity of a resource expressed as an absolute value representing page importance. Each search engine uses its own algorithms to estimate a page citation index. As a rule, these values are not published.

As well as the absolute citation index value, a scaled citation index is sometimes used. This relative value indicates the popularity of a page relative to the popularity of other pages on the Internet. You will find a detailed description of citation indexes and the algorithms used for their estimation in the next sections.

**3.3 Link text (anchor text)**

The link text of any inbound site link is vitally important in search result ranking. The anchor (or link) text is the text between the HTML tags «A» and «/A» and is displayed as the text that you click in a browser to go to a new page. If the link text contains appropriate keywords, the search engine regards it as an additional and highly significant recommendation that the site actually contains valuable information relevant to the search query.

**3.4 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. *

**3.5 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.

**3.6 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.