Information about How Google Works

How Google Works,Page Rank Technology and examples explaining Page Rank Technology.

Contents.. • Uncover the secrets of Google. • How it all works. • Understanding the technology „PAGE RANK‟ behind it. • Why „PAGE RANK „ is a pioneering technology. • Reference.

Why is Google Different ??

Step1… • Exploring the web…

Crawling… • Special Software known as “Googlebot” is used. • Runs on large number of computers to crawl the web. • Googlebot starts from its last crawl status and busily looks for new sites,change to current and invalid links.

Step 2… • Organizing the data.. • Report on the pages visited & thus index is updated. • Index like something at back of the book.

Step3… • Presenting the Data.. • Google search doesn‟t just drive into index & fish around for what it needs. • Use of Knowledge Graph. • Several factors are used to present the most relevant search results.

Factors… Some of the know factors.. • Type of content. • Quality of content. • Freshness of content. • The user‟s region. • Legitimacy of the site. • Name and address of the website. • Search word synonyms. • Social media promotions. • How many links point to a particular web page. • The value of those links.

Page Rank… • Developed by Larry Page and Sergey Brin in 1998 • Trademark of Google • Patented by Stanford Unvirsity • Back bone of Google Search Technology

UNDERSTANDING PAGE RANK

Page Rank Technology.. • Rank pages based on the number of other pages that link to it. • Gives an indication of the relative importance of a page. • Hence,an appropriate „SERP‟(Search Engine Result Page) listing. • Calculated by nature and number of „ back links „

Definition of Page Rank.. • “We assume page A has pages T1…Tn which point to it.The parameter „d‟ is a damping factor which can be set between 0 & 1. We usually set „d‟ to 0.85.Also C(A) is defined as the number of links going out of page A. The PageRank of a page A is given as follows: PR(A) = (1-d) + d*(PR(T1)/C(T1)+…+PR(Tn)/C(Tn)) Note that the Page Ranks form a probability distribution over web pages , so the sum of all web pages , Page Ranks will be one”

Calculating Page Rank...

Definition Of Terms • PR: Shorthand for PageRank: the actual , real ,page rank for each page as calculated by Google. • Back link : If page A links out to page B , the page B is said to have a „ back link‟ from page A.

PR(A) =(1-d)+d(PR(T1)/C(T1)+..+PR(Tn)/C(Tn)) • The PR of each page depends on the PR of the pages pointing to it. • We won‟t know what PR those pages have until the pages pointing to them have their PR calculated. • ………….and so on..

Seems impossible to do this calculation….

BUT THERE IS A SOLUTION

• Page Rank can be calculated using simple iterative algorithms . • What we need to do .. • *Remember the each value we calculate. • *Repeat the calculations lots of times.

How Many Times ???

Until the number stop changing much…

Page A Page B • Let ,us assume that PR =1.0 & d=0.85 PR(A) = (1-d) + d(PR(B)/1) PR(B) =(1-d)+d(PR(A)/1) On calculation.. PR(A) = 0.15+0.85*1 =1 PR(B) = 0.15 + 0.85*1=1

OK BUT WHY SHOULD I ASSUME PR =1 ? WHAT IF NOT…

So , Lets start with PR=0 • PR(A) =0.15 +0.85 *0=0.15 • PR(B)=0.15 + 0.85*0.15=0.2775 • Again • PR(A) =0.15 +0.85 *0.2775=0.385875 • PR(B)=0.15 + 0.85*0.385875=0.47799375 And Again • PR(A) =0.15 +0.85 *0.47799375=0.5562946875 • PR(B)=0.15 + 0.85*0.5562946875=0.622850484375 Inference : PR approaches 1.. •

Example… • Let us assume :PR(A)=40,PR(B)=40 • First calculation: • PR(A)=0.15+0.85*40=34.15 • PR(B)=0.15+0.85*34.15=29.1775 • And again : • PR(A)=0.15 + 0.85*29.1775=24.950875 • PR(B)=0.15 + 0.85*24.950875=21.35824375 • ………PR will approach and settle down @1

Another Example…

• The home page has got the highest PR…after all it is the one getting most numbers of incoming.. • But what's happened to the average ? It‟s only 0.378 !!!

Lets, take a look at the “external site “ pagesWhat’s happening to their Page Rank ?

• That‟s better- It does work after all !! • And look at the PR of our home page !! • All those incoming links sure make a difference

Regardless the number of pages, average PR will always be 1.0 at best. And that’s how you searching happens on GOOGLE.

References… • Sergey Brin & Larry Page , “Anatomy of Large-Scale Hyper textual Web Search Engine” • http://www.cs.princeton.edu/~chazelle/courses/BIB /pagerank.htm • http://en.wikipedia.org/wiki/PageRank • http://www.whitelines.nl/html/google-pagerank.html • http://www.google.co.in/insidesearch/howsearchw orks/thestory/

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