Many Business owners get confused about Page Rank at Link building. So what is Page Rank and how does it affect your web site. How do Google use Page Rank? and should you be worried if you haven't or don't appear to have any Page Rank?

Let's try to keep it simple "well that's tough because it isn't simple" but we'll do our best.

The Anatomy of a Large-Scale Hyper textual Web Search Engine

was a document created by Sergey Brin and Lawrence Page while at Stamford university. The paper was in fact a visionary piece of thinking. What they were trying to do was create a collection of documents found on the web using crawlers, robots or spiders and then index them for fast searching using in principal a database structure (fat tables).

This is the actual text from a small section of the paper that talks about Page Rank

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2.1.1 Description of Page Rank Calculation

Academic citation literature has been applied to the web, largely by counting citations or back links to a given page. This gives some approximation of a page's importance or quality. Page Rank extends this idea by not counting links from all pages equally, and by normalizing by the number of links on a page. Page Rank is defined as follows:

We assume page A has pages T1...Tn which point to it (i.e., are citations). The parameter d is a damping factor which can be set between 0 and 1. We usually set d to 0.85. There are more details about d in the next section. Also C(A) is defined as the number of links going out of page A. The Page Rank of a page A is given as follows:

PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))

Note that the Page Ranks form a probability distribution over web pages, so the sum of all web pages' Page Ranks will be one.

Page Rank or PR(A) can be calculated using a simple iterative algorithm, and corresponds to the principal eigenvector of the normalized link matrix of the web. Also, a Page Rank for 26 million web pages can be computed in a few hours on a medium size workstation. There are many other details which are beyond the scope of this paper.

 

2.1.2 Intuitive Justification

Page Rank can be thought of as a model of user behavior. We assume there is a "random surfer" who is given a web page at random and keeps clicking on links, never hitting "back" but eventually gets bored and starts on another random page. The probability that the random surfer visits a page is its Page Rank. And, the d damping factor is the probability at each page the "random surfer" will get bored and request another random page. One important variation is to only add the damping factor d to a single page, or a group of pages. This allows for personalization and can make it nearly impossible to deliberately mislead the system in order to get a higher ranking. We have several other extensions to Page Rank, again see [Page 98].

Another intuitive justification is that a page can have a high Page Rank if there are many pages that point to it, or if there are some pages that point to it and have a high Page Rank. Intuitively, pages that are well cited from many places around the web are worth looking at. Also, pages that have perhaps only one citation from something like the Yahoo! homepage are also generally worth looking at. If a page was not high quality, or was a broken link, it is quite likely that Yahoo's homepage would not link to it. Page Rank handles both these cases and everything in between by recursively propagating weights through the link structure of the web.

Ok so now we know but is it still the same today.

The Characteristics of Page Rank

The characteristics of Page Rank are illustrated in this example.

In a simple web site consisting of three pages A, B and C, page A links to pages B and C, page B links to page C and C links to page A.

 

According to Page and Brin, the damping factor d is usually set to 0.85, but to keep the calculation simple we set it to 0.5. The exact value of the damping factor d does effect Page Rank, but the fundamental principles of Page Rank remains the same. So, we get the following equations for the Page Rank calculation:

PR(A) = 0.5 + 0.5 PR(C)
PR(B) = 0.5 + 0.5 (PR(A) / 2)
PR(C) = 0.5 + 0.5 (PR(A) / 2 + PR(B))

These equations can easily be solved. We get the following Page Rank values for the single pages:

PR(A) = 14/13 = 1.07692308
PR(B) = 10/13 = 0.76923077
PR(C) = 15/13 = 1.15384615

It's obvious that the sum of all the pages' Page Rank is 3 and thus equals the total number of web pages. As shown above this is not a specific result for our simple example.

For our simple three-page example it is easy to solve the according equation system to determine Page Rank values. In practice, the web consists of over a trillion documents and it is not possible to find a solution by inspection.

The Iterative Computation of Page Rank

Because of the size of the actual web, the Google search engine uses an approximate, iterative computation of Page Rank values. This means that each page is assigned an initial starting value and the Page Ranks of all pages are then calculated in several computation circles based on the equations determined by the Page Rank algorithm. The iterative calculation is illustrated by this three-page example, where each page is assigned a starting Page Rank value of 1.

Iteration	PR(A)	PR(B)	PR(C)
0	1	1	1
1	1	0.75	1.125
2	1.0625	0.765625	1.1484375
3	1.07421875	0.76855469	1.15283203
4	1.07641602	0.76910400	1.15365601
5	1.07682800	0.76920700	1.15381050
6	1.07690525	0.76922631	1.15383947
7	1.07691973	0.76922993	1.15384490
8	1.07692245	0.76923061	1.15384592
9	1.07692296	0.76923074	1.15384611
10	1.07692305	0.76923076	1.15384615
11	1.07692307	0.76923077	1.15384615
12	1.07692308	0.76923077	1.15384615

See how you get a good approximation of the real Page Rank values after only a few iterations. As few as 100 iterations are necessary to get a good approximation of the Page Rank values of the whole web.

Also, by means of the iterative calculation, the sum of all pages' Page Ranks still converges to the total number of web pages. So the average Page Rank of a web page is 1. The minimum Page Rank of a page is given by (1-d). Therefore, there is a maximum Page Rank for a page which is given by dN+(1-d), where N is total number of web pages. This maximum can theoretically occur, if all web pages solely link to one page, and this page also solely links to itself.

 

Once you get this you'll understand the importance of site structure. because you can affect the page rank of a web site via design alone. This is sometimes reffered to as page rank sculpting Here is a simple video to explain more.

 

In this next video you can get over 1 hours insight into how Google critique some real web sites.

 

Ok its simple Links are very important but don't buy Page Rank it won't work but do take look for free links or reciprocal links and back links. These are all useful in enhancing your positions in Google.