PR(A) = (1-d) + d(PR(t1)/C(t1) + … + PR(tn)/C(tn))

That’s the equation that calculates a page’s PageRank. It’s the original one that was published when PageRank was being developed, and it is probable that Google uses a variation of it but they aren’t telling us what it is. It doesn’t matter though, as this equation is good enough.

In the equation ‘t1 – tn’ are pages linking to page A, ‘C’ is the number of outbound links that a page has and ‘d’ is a damping factor, usually set to 0.85.

We can think of it in a simpler way:-

a page’s PageRank = 0.15 + 0.85 * (a “share” of the PageRank of every page that links to it)

“share” = the linking page’s PageRank divided by the number of outbound links on the page.

A page “votes” an amount of PageRank onto each page that it links to. The amount of PageRank that it has to vote with is a little less than its own PageRank value (its own value * 0.85). This value is shared equally between all the pages that it links to.

From this, we could conclude that a link from a page with PR4 and 5 outbound links is worth more than a link from a page with PR8 and 100 outbound links. The PageRank of a page that links to yours is important but the number of links on that page is also important. The more links there are on a page, the less PageRank value your page will receive from it.

If the PageRank value differences between PR1, PR2,…..PR10 were equal then that conclusion would hold up, but many people believe that the values between PR1 and PR10 (the maximum) are set on a logarithmic scale, and there is very good reason for believing it. Nobody outside Google knows for sure one way or the other, but the chances are high that the scale is logarithmic, or similar. If so, it means that it takes a lot more additional PageRank for a page to move up to the next PageRank level that it did to move up from the previous PageRank level. The result is that it reverses the previous conclusion, so that a link from a PR8 page that has lots of outbound links is worth more than a link from a PR4 page that has only a few outbound links.

Whichever scale Google uses, we can be sure of one thing. A link from another site increases our site’s PageRank. Just remember to avoid links from link farms.

Note that when a page votes its PageRank value to other pages, its own PageRank is not reduced by the value that it is voting. The page doing the voting doesn’t give away its PageRank and end up with nothing. It isn’t a transfer of PageRank. It is simply a vote according to the page’s PageRank value. It’s like a shareholders meeting where each shareholder votes according to the number of shares held, but the shares themselves aren’t given away. Even so, pages do lose some PageRank indirectly, as we’ll see later.

Ok so far? Good. Now we’ll look at how the calculations are actually done.

For a page’s calculation, its existing PageRank (if it has any) is abandoned completely and a fresh calculation is done where the page relies solely on the PageRank “voted” for it by its current inbound links, which may have changed since the last time the page’s PageRank was calculated.

The equation shows clearly how a page’s PageRank is arrived at. But what isn’t immediately obvious is that it can’t work if the calculation is done just once. Suppose we have 2 pages, A and B, which link to each other, and neither have any other links of any kind. This is what happens:-

Step 1: Calculate page A’s PageRank from the value of its inbound links

Page A now has a new PageRank value. The calculation used the value of the inbound link from page B. But page B has an inbound link (from page A) and its new PageRank value hasn’t been worked out yet, so page A’s new PageRank value is based on inaccurate data and can’t be accurate.

Step 2: Calculate page B’s PageRank from the value of its inbound links

Page B now has a new PageRank value, but it can’t be accurate because the calculation used the new PageRank value of the inbound link from page A, which is inaccurate.

It’s a Catch 22 situation. We can’t work out A’s PageRank until we know B’s PageRank, and we can’t work out B’s PageRank until we know A’s PageRank.

Now that both pages have newly calculated PageRank values, can’t we just run the calculations again to arrive at accurate values? No. We can run the calculations again using the new values and the results will be more accurate, but we will always be using inaccurate values for the calculations, so the results will always be inaccurate.

The problem is overcome by repeating the calculations many times. Each time produces slightly more accurate values. In fact, total accuracy can never be achieved because the calculations are always based on inaccurate values. 40 to 50 iterations are sufficient to reach a point where any further iterations wouldn’t produce enough of a change to the values to matter. This is precisiely what Google does at each update, and it’s the reason why the updates take so long.

One thing to bear in mind is that the results we get from the calculations are proportions. The figures must then be set against a scale (known only to Google) to arrive at each page’s actual PageRank. Even so, we can use the calculations to channel the PageRank within a site around its pages so that certain pages receive a higher proportion of it than others.

NOTE:
You may come across explanations of PageRank where the same equation is stated but the result of each iteration of the calculation is added to the page’s existing PageRank. The new value (result + existing PageRank) is then used when sharing PageRank with other pages. These explanations are wrong for the following reasons:-

1. They quote the same, published equation – but then change it

from PR(A) = (1-d) + d(……) to PR(A) = PR(A) + (1-d) + d(……)

It isn’t correct, and it isn’t necessary.

2. We will be looking at how to organize links so that certain pages end up with a larger proportion of the PageRank than others. Adding to the page’s existing PageRank through the iterations produces different proportions than when the equation is used as published. Since the addition is not a part of the published equation, the results are wrong and the proportioning isn’t accurate.

According to the published equation, the page being calculated starts from scratch at each iteration. It reliessolely on its inbound links. The ‘add to the existing PageRank’ idea doesn’t do that, so its results are necessarily wrong.

In a nutshell, it considers links to be like votes. In addition, it considers that some votes are more important than others. PageRank is Google’s system of counting link votes and determining which pages are most important based on them. These scores are then used along with many other things to determine if a page will rank well in a search.

WHAT IS ALEXA RANKING ?

Alexa collects information from users who have installed an “Alexa Toolbar,” allowing them to provide statistics on web site traffic, as well as lists of related links.

Although Alexa ranking doesnt directly affect your website ranking, but it does give you an indication as to how popular your website is.

Alexa’s traffic rankings are based on the usage patterns of Alexa Toolbar users over a rolling 3 month period. A site’s ranking is based on a combined measure of reach and pageviews. Reach is determined by the number of unique Alexa users who visit a site on a given day. Pageviews are the total number of Alexa user URL requests for a site. However, multiple requests for the same URL on the same day by the same user are counted as a single pageview. The site with the highest combination of users and pageviews is ranked #1.

Are you struggling with search engine optimisation and need some help? We’ve got the tools you need!

In our articles section you will find the information you need to optimise your pages, make them rank high and keep them that way.

Our free website rankings tools include a Google keyword checker, meta tag generator and free website statistics tracking package that you can use to monitor your traffic. In addition we’ve added some useful tools in our resources section that will help you gain traffic to your website.

We will be constantly refining and adding to the free tools section and adding more articles to educate the budding webmaster over the coming weeks.

We’ve recently added a quick tutorial section which will give you the background you need to begin optimising your pages for the search engines and gaining high rankings. Start with learning how to write your page titles and meta tags correctly and then move on to some of the more advanced techniques like publishing articles and progressive linking campaigns.

We’ve also covered some of the basics of website design and included some information on using website templates and do’s and don’ts for using some advanced scripting in DHTML, Flash and javascript.

Of course, the search engines don’t always get it right. Non-relevant pages make it through, and sometimes it may take a little more digging to find what you are looking for. But, by and large, search engines do an amazing job.

As WebCrawler founder Brian Pinkerton puts it, “Imagine walking up to a librarian and saying, ‘travel.’ They’re going to look at you with a blank face.”

OK — a librarian’s not really going to stare at you with a vacant expression. Instead, they’re going to ask you questions to better understand what you are looking for.

Unfortunately, search engines don’t have the ability to ask a few questions to focus your search, as a librarian can. They also can’t rely on judgment and past experience to rank web pages, in the way humans can.

So, how do crawler-based search engines go about determining relevancy, when confronted with hundreds of millions of web pages to sort through? They follow a set of rules, known as an algorithm. Exactly how a particular search engine’s algorithm works is a closely-kept trade secret. However, all major search engines follow the general rules below.