inside page ranking

8/14/2019 inside page ranking

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INS IDE PAGERANKINS IDE PAGERANK

Bejo Thampi

S7 CSE

Roll No:-14

SNGCE

On :-26/08/2009

Guided By : Mr. S ujith Kumar


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CONTENTSCONTENTS

Introduction

PageRank

PageRank calculation

Optimization of PageRankRemoval of Dangling Pages

Conclusion

References


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IntroductionIntroduction

Represents how important a page is on theweb.

Numeric value between 0 and 10

Old Algorithms considered only the content

of the page causing web spamming

Considers both content and anchor text

(BackLinks)


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PageRankPageRank

PageRank is a link analysis algorithm.

Determines a pages ranking in the searchresults.

A trademark of Google which is patented to

S tanford University.


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ContinueContinue

Quoting from the original Google paper,PageRank is defined as:

a link from page A to page B as a vote,by page A, for Page B.

it also analyzes the page that casts thevote.


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Calculation of PageRankCalculation of PageRank

Two Methods

S implified Algorithm and Damping

Algorithm

Based probability distribution


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S implified Algorithm

In the general case, the PageRank value

for any page u can be expressed as:


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Damping Algorithm

PR(V)= (1-d) + d(PR(V2)/1)

d (damping factor) =0.85

Calculated us ing S imple Iterative Method


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ExampleExample

Each page has one outgoing link

i.e. hA = 1 and hB = 1

Page A Page B


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S tart a guess at 1.0

PR (A) = (1 d) + d(PR (B)/1)

PR (B) = (1 d) + d(PR (A)/1)

i.e. PR (A) = 0.15 + 0.85 * 1 = 1PR(B) = 0.15 + 0.85 * 1 = 1

numbers arent changing. S o here weconsider this as a good guess.

Guess1Guess1


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Guess2Guess2

guess at 0 instead and re-calculate:

PR(A) = 0.15 + 0.85 * 0 = 0.15

PR (B) = 0.15 + 0.85 * 0.15= 0.2775

And again:

PR(A) = 0.15 + 0.85 * 0.2775= 0.385875PR (B) = 0.15 + 0.85 * 0.385875= 0.47799375


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ContinueContinue

On 39th iteration ,PR(A)=.999999 and PR (B)=1.0000000

On40th iteration,

PR(A)=1.000 and PR(B)=1.000.

And average PageRank =1.00000

Principle:- the normalized probabilitydistribution (the average PageR ank for allpages) will be 1.0.


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VariationsVariations

Google Toolbar Rank

The Google Toolbar's PageRank feature

displays a visited page's PageRank as awhole number between 0 and 10.

S ER P Rank

Result returned by search engine in

response to a keyword query.


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Optimization of PageRankOptimization of PageRank

Three fundamental areas to look at when

trying to optimize the PageR ank for site:

1. The links you choose to have link to you,

i.e., which ones you choose, and how much

effort you put in to getting them.


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2. Who you choose to link out to from yoursite

Maximising PageRank Feedback and minimisingPageRank leakage

3.The internal navigational structure and

linkage of your pages

distribute PageR ank within your site.

ContinueContinue


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Internal Linking

Hierarchical

Courtsey: http://www-db.Stanford.edu/~backrub/google.html


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ContinueContinue

Looping:-

Courtsey: http://www-db.Stanford.edu/~backrub/google.html


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Removal of Dangling PagesRemoval of Dangling Pages

S imply links that point toany page with no outgoing

links.

These links are simplepages that not downloaded

yet.

Ranking is affected.

Need to be removedhttp://www.iprcom.com/papers/pagerank/index.html


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ContinueContinue

E liminates dangling pages before

PageRank Calculation.

Done by introducing a dummy page.

A link to itself and is pointed by every

dangling page.


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ContinueContinue

Courtsey: http://www.iprcom.com/papers/pagerank/index.html


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ConclusionConclusion

Parameter involved in Googles ranking of

the answers to a given query.

Optimal computation of PageR ank

Optimization of PageRank


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ReferencesReferences

1. The Google S tory - David A Vise

2. htp://www.iprcom.com/papers/pagerank/index.html

3. http://en.wikipedia.org/wiki/PageRank

4. http://www.siteall.com/guide/


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THANK YOUTHANK YOU


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QUESTIONSQUESTIONS? ?? ?

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