SEO (8) Page Rank and Trust Rank Algorithms

Page Rank and Trust Rank Algorithms

Google determines rankings of its search result listings using PageRank and TrustRank algorithms. It is important to understand these algorithms since the higher one’s website ranks in search engine results, higher the potential to gain more targeted visitors.

PageRank : The rank of a webpage in organic search results of Google is determined by PageRank.

PR(A)=(1-d) + d[PR(T1)/C(T1) + … + PR(Tn)/ C(Tn)]

where

PR(A) is Page Rank of web page A

T1…Tn are web pages that point to page A

d is damping factor which can be set between 0 and 1. It is usually set to 0.85

C(A) are the number of links going out from web page A

PR(A) is based on the concept that a random surfer who is given a web page A keeps clicking on links at random until he gets bored. The surfer never hits the back button. On getting bored, the random surfer requests a random web page. The probability that a surfer visits a page A is PR(A). The damping factor d is the probability that at each page, the surfer gets bored and requests another random web page. A variation that is added to the PageRank calculation is that different damping factors may be assigned different pages T1…Tn which link to page A.

One can conclude from the PageRank equation that:

1. The more inbound links a web page has, the higher the PageRank

2. It is better to have inbound links from a web page that has high PageRank and few out links over a webpage with high PageRank and too many out links.

e.g. PR(X) = 4 and C(X) = 5 then d[PR(X)/C(X)] = 0.85d

PR(Y) = 8 and C(Y) = 100 then d[PR(Y)/C(Y)] = 0.085d

PageRank forms a probability distribution over web pages, so the sum of all web pages’ PageRanks will be 1. PR(A) can be calculated using an iterative algorithm, and corresponds to principal eigenvector of the normalized link matrix of the web .

PR(A1) + PR(A2) + PR(A3) + … + PR(An) = 1

PR(A) = (1-d) if web page A has no inbound links.

There are hundreds of web pages added to the World Wide Web every moment. Since sum of PageRank of all web pages over the WWW is a constant i.e. 1, this means that as more pages are added to the WWW, PageRank of each web page gets constantly updated to accommodate the PageRank of new web pages’. Assume that, if a web page has no inbound links, (1-d)≈ 0. As inbound links increase the PageRank of a webpage, one can conclude that outbound links decrease the PageRank of a web page. This decrease in PageRank of a webpage due to outbound links is called PageRank Leak.

To ensure a high PageRank it is necessary that:

1. A web page should have high number of inbound links

2. A web page should have low number of outbound links

The PageRank algorithm determines the importance of a web site by counting the number of inbound links. This concept can be manipulated by artificially inflating the number of inbound links to a web page. PageRank also does not incorporate the quality of the web page in its calculations. Hence Google is developing the TrustRank algorithm and has registered the trademark for TrustRank on March 16, 2005.

TrustRank : According to Gyongyi, Garcia-Molina and Pederson, the proposed algorithms for TrustRank rely on the PageRank algorithm. This algorithm takes into account, not only the inbound links to a web page but also the quality of the web page. To determine the quality of a web page, a panel of human experts will identify a set of reputable web pages that will act as the seed for the spider. This algorithm is based on an empirical observation that: good pages seldom point to bad ones.

One can conclude that a web page can achieve higher TrustRank if:

1. Reputable (good) web pages link to the web page

2. The web page does not link to any bad web pages

3. The web page does not mislead the search engine or employ search engine spam

source:SEARCH ENGINE OPTIMIZATION AND MARKETING by Binoy Varghese page 17-20

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