# Inside the Google PageRank Algorithm

Delving into the inner-workings of Google PageRank algorithm and how it affects results.

# What Is PageRank?

PageRank is the algorithm used by the Google search engine, originally formulated by Sergey Brin and Larry Page in their paper "The Anatomy of a Large-Scale Hypertextual Web Search Engine."

It is based on the premise, prevalent in the world of academia, that the importance of a research paper can be judged by the number of citations the paper has from other research papers. Brin and Page have simply transferred this premise to its web equivalent: the importance of a web page can be judged by the number of hyperlinks pointing to it from other web pages.

# So What Is the Algorithm?

It may look daunting to nonmathematicians, but the Google PageRank algorithm is in fact elegantly simple and is calculated as follows:

where:

• PR(A) is the Google PageRank of a page A.
• PR(T1) is the Google PageRank of a page T1.
• C(T1) is the number of outgoing links from the page T1.
• d is a damping factor in the range 0 « d « 1, usually set to 0.85.

The PageRank of a web page is therefore calculated as a sum of the Google PageRanks of all pages linking to it (its incoming links), divided by the number of links on each of those pages (its outgoing links).

# And What Does This Mean?

From a search engine marketer's point of view, this means there are two ways in which PageRank can affect the position of your page on Google:

• The number of incoming links. Obviously the more of these, the better. But there is another thing the algorithm tells us: no incoming link can have a negative effect on the Google PageRank of the page it points at. At worst, it can simply have no effect at all.
• The number of outgoing links on the page that points to your page. The fewer of these, the better. This is interesting: it means given two pages of equal PageRank linking to you, one with 5 outgoing links and the other with 10, you will get twice the increase in PageRank from the page with only 5 outgoing links.

At this point, we take a step back and ask ourselves just how important PageRank is to the position of your page in the Google search results.

The next thing we can observe about the Google PageRank algorithm is that it has nothing whatsoever to do with relevance to the search terms queried. It is simply one single (admittedly important) part of the entire Google relevance ranking algorithm.

Perhaps a good way to look at PageRank is as a multiplying factor, applied to the Google search results after all its other computations have been completed. The Google algorithm first calculates the relevance of pages in its index to the search terms, and then multiplies this relevance by the Google PageRank to produce a final list. The higher your PageRank, therefore, the higher up the results you will be, but there are still many other factors related to the positioning of words on the page that must be considered first.

# So What's the Use of the Google PageRank Calculator?

If no incoming link has a negative effect, surely I should just get as many as possible, regardless of the number of outgoing links on its page?

Well, not entirely. The PageRank algorithm is very cleverly balanced. Just like the conservation of energy in physics with every reaction, PageRank is also conserved with every calculation. For instance, if a page with a starting PageRank of 4 has two outgoing links on it, we know that the amount of PageRank it passes on is divided equally between all its outgoing links. In this case, 4 / 2 = 2 units of PageRank is passed on to each of 2 separate pages, and 2 + 2 = 4 - so the total PageRank is preserved!

 There are scenarios where you may find that total PageRank is not conserved after a calculation. PageRank itself is supposed to represent a probability distribution, with the individual PageRank of a page representing the likelihood of a "random surfer" chancing upon it.

On a much larger scale, supposing Google's index contains a billion pages, each with a PageRank of 1, the total PageRank across all pages is equal to a billion. Moreover, each time we recalculate PageRank, no matter what changes in PageRank may occur between individual pages, the total PageRank across all 1 billion pages will still add up to a billion.

Second, if we assume that every new page in Google's index begins its life with a PageRank of 1, there is a way we can increase the combined PageRank of pages within our site - by increasing the number of pages! A site with 10 pages will start life with a combined PageRank of 10, which is then redistributed through its hyperlinks. A site with 12 pages will therefore start with a combined PageRank of 12. We can thus improve the Google PageRank of our site as a whole by creating new content (i.e., more pages), and then control the distribution of that combined PageRank through strategic interlinking between the pages.

And this is the purpose of the Google PageRank Calculator - to create a model of the site on a small scale including the links between pages, and see what effect the model has on the distribution of PageRank.

# How Does the Google PageRank Calculator Work?

To get a better idea of the realities of PageRank, visit the Google PageRank Calculator (http://www.markhorrell.com/seo/pagerank.asp).

It's very simple really. Start by typing in the number of interlinking pages you wish to analyze and hit Submit. I have confined this number to just 20 pages to ease server resources. Even so, this should give a reasonable indication of how strategic linking can affect the Google PageRank distribution.

Next, for ease of reference once the calculation has been performed, provide a label for each page (e.g., Home Page, Links Page, Contact Us Page, etc.) and again hit Submit.

Finally, use the list boxes to select which pages each page links to. You can use Ctrl and Shift to highlight multiple selections.

You can also use this screen to change the initial PageRanks of each page. For instance, if one of your pages is supposed to represent Yahoo!, you may wish to raise its initial PageRank to, say, 3. However, in actual fact, starting PageRank is irrelevant to its final computed value. In other words, even if one page were to start with a PageRank of 100, after many iterations of the equation (see below), the final computed PageRank will converge to the same value as it would had it started with: a PageRank of only 1!

You can play around with the damping factor d, which defaults to 0.85 as this is the value quoted in Brin and Page's research paper.

- Mark Horrell