Presented by:
Amit Kumar Pathak

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EFFICIENT SEARCHING USING PAGE RANK
INTRODUCTION
Search Engines have become an integral part of the World Wide Web.
One such search engine proposed by Larry Page and Sergey Brin known as GOOGLE.
Most widely accepted search engine.
Works on its own unique patented algorithm known as Page Rank.
WHAT IS PAGE RANK ??
PageRank is a numeric value.
It represents how important a page is on the web.
PageRank is Google's way of deciding a page's importance.
It isn't the only factor that Google uses to rank pages, but it is an important one.
When one page links to another page, it is effectively casting a vote for the other page.
The more votes that are cast for a page, the more important the page must be.
The importance of the page that is casting the vote also determines how important the vote itself is.
Google calculates a page's importance from the votes cast for it.
HISTORY
PageRank was developed at Stanford University by Larry Page and later Sergey Brin as part of a research project about a new kind of search engine.
Project started in 1995 and led to a functional prototype, named Google, in 1998.
PageRank is based on citation analysis that was developed in the 1950s by Eugene Garfield at the University of Pennsylvania
Google's founders have cited Garfield's work in their original paper.
Web page link analysis first developed by Jon Kleinberg and his team.
PAGE RANK CONCEPT
The occurrence of a search phrase within a document is the ethical ranking technique of most search engines.
For the purpose of better search results the concept of page link popularity was developed.
This also make search engines resistant against automatically generated web pages based upon the analysis of content specific ranking criteria.
Following this concept, the number of inbound links for a document measures its general importance.
Hence, a web page is generally more important, if many other web pages page link to it.
WHY PAGE RANK IS DIFFERENT
Page Rank is not simply based upon the total number of inbound links.
In Page Rank concept, all inbound links are not equal.
The rank of the documents which page link to the main page is also important.
PageRank is based on the linking structure of the whole web.
DESCRIPTION OF PAGE RANK CALCULATION
Page Rank works largely by counting citations or back links to a given page.
Page Rank then extends this idea by not counting links from all pages equally, and by normalizing by the number of links on a page.
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 (usually 0.85).
C(A) is defined as the number of links going out of page A.
Then Page Rank of page A is given as:
PR(A) = (1-d) + d (PR(T1)/C(T1) +...+PR(Tn)/C(Tn))
DAMPING FACTOR
The Page Rank theory holds that even an imaginary surfer who is randomly clicking on links will eventually stop clicking.
The probability, at any step, that the person will continue is a damping factor d.
This is generally set to 0.85
The damping factor is subtracted from 1.
This term is then added to the product of the damping factor and the sum of the incoming Page Rank scores.
So any page's Page Rank is derived in large part from the Page Ranks of other pages.
The damping factor adjusts the derived value downward.
The sum of all Page Ranks will be one due to probability distribution over the web pages.
The sum of all page ranks converges to the total number of web pages.
Page Rank Algorithm
PAGE RANK ALGORITHM
PR(A) = (1-d) + d (PR(T1)/C(T1) +...+PR(Tn)/C(Tn))
PR(A) is the Page Rank of page A,
PR(Ti) is the Page Rank of pages Ti which page link to page A,
C(Ti) is the number of outbound links on page Ti and
d is a damping factor.
EXAMPLE
We regard a small web consisting of three pages A, B and C.
Page A links to the pages B and C, page B links to page C and page C links to page A.
Here we assume d=0.50 to keep the calculation simple
PR(A) = 0.5 + 0.5 PR©PR(B) = 0.5 + 0.5 (PR(A) / 2)PR© = 0.5 + 0.5 (PR(A) / 2 + PR(B))
On solving these, we get:
PR(A) = 14/13 = 1.07692308PR(B) = 10/13 = 0.76923077PR© = 15/13 = 1.15384615
Sum of these=3=number of web pages
AUTHORS FINDINGS
FACTORS AFFECTING PAGE RANK
Visibility of a link
Position of a page link within a document
Distance between web pages
Importance of a linking page
Up-to-dateness of a linking page.
FLAWS IN PAGE RANK
Page Rank can be manipulated. e.g. Google Bombs.
Using “Link Farming”
HOW TO IMPROVE YOUR WEBSITES PAGE RANK
Update your website every day by adding more unique content.
Provide inside linking to your website.
Create sitemap for your website.
Trade your page link with other web owners.
CONCLUSION
Frequent content updates don’t improve Page Rank automatically. Content is not part of the PR calculation.
Sub-directories don’t necessarily have a lower Page Rank than root-directories.
Wikipedia links don’t improve PageRank automatically.
Efficient internal onsite linking has an impact on PageRank.
Links from and to high quality related sites have an impact on Page Rank.
Multiple votes to one page link from the same page cost as much as a single vote.