1. Search Engine Optimization
and Page Rank Algorithms
NILKANTH SATISH SHET
NILKANTH SATISH SHET SHIRODKAR
CEO at Redicals.com
Search Engine Basics
Search engines are programs that search
documents for specified keywords and
returns a list of the documents where the
keywords were found.
Some of the popular Search Engine are
Working of Search Engine
●A Search Engine operates is a three
●Ranking and Serving Results
Types of Search Engine
Search Engine are categorised in two
categories based on their result
●Crawler-Based Search Engines
6. Search Engine Basics
●Crawler-Based Search Engines
These Search Engine create their listings
automatically by “crawl” or “spider” the web.
● Human-Powered Directories
A human-powered directory, such as the
Open Directory, depends on humans for its
7. Page Rank
Developed by Larry Page and Sergey Brin in 1998
Trademark of Google
Patented by Stanford University
Back bone of Google Search Technology
8. Page Rank Technology :-
Ranks 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 listing
Calculated by nature and number of back links
Scale : 0 — 10
9. PageRank algorithm
PR(A) = (1-d) + d (PR(TI)/C(T1) + + PR(Tn)/C(Tn))
PR(A) is the PageRank of page A,
PR(Ti) is the PageRank of pages Ti which link to page A,
C(Ti) is the number of outbound links on page Ti
d is a damping factor which can be set between 0 and 1.
10. Let us assume page A has pages Ti ...Tn, The
parameter d is a damping factor which can be set
between 0 and 1. We usually set d to 0.85. Also C(A)
is defined as the number of links going out of page A.
11. PR(T1)/C(T1) + ... + PR(Tn)/C(Tn)
The PageRank of pages Ti which link to page A does
not influence the PageRank of page A uniformly.
The PageRank of a page T is always weighted by the
number of outbound links C(T) on page T.
Which means that the more outbound links a page T
has, the less will page A benefit from a link to it on page
12. PR(T1)/C(T1) + ... + PR(Tn)/C(Tn)
The weighted PageRank of pages Ti is then added up.
The outcome of this is that an additional inbound link for
page A will always increase page A's PageRank.
13. PR(A) = (1-d) + d * (PR(T1)/C(T1)
+ ... + PR(Tn)/C(Tn))
After all, the sum of the weighted PageRanks of all
pages Ti is multiplied with a damping factor d which can
be set between 0 and 1. Thereby, the extend of
PageRank benefit for a page by another page linking to
it is reduced.
15. PageRank Algorithm
PageRank can be calculated using a simple iterative
We can calculate a page's PR without knowing the final
value of the PR of the other pages.
What we need to do :-
• Remember the each value we calculate
• Repeat the calculations lots of times
●The ranking is the basic criteria by which
organic Search Engines index and then
●Page rank calculation tools
21. Adv a ntages of PageRank
The algorithm is robust against Spam since its not
easy for a webpage owner to add inlinks to his/her
page from other important pages.
PageRank is a global measure and is query
22. Disdvantages of PageRank
The major disadvantage of PageRank is that it favors the older
pages, because a new page, even a very good one will not
have many links unless it is a part of an existing site.
PageRank can be easily increased by the concept of ”link-farms”
as shown below. However, while indexing, the search
actively tries to find these flaws.
23. Google Tools
Find the source of your visitors, what they're viewing, and
Google Website Optimizer
Run experiments on your pages to see what will work and
Google Webmaster Tools
Optimize how Google interacts with your website.
•Comparative Study of HITS and PageRank Link
based Ranking Algorithms by Pooja Devi, Ashlesha
Gupta, Ashutosh Dixit International Journal of
Advanced Research in Computer and
•SEO TECHNIQUES AND THEIR EFFECTIVENESS
ON GOOGLE’S SEARCH RANKING ALGORITHM By
Edgar Damian Ochoa, CALIFORNIA STATE
•Search Engine Optimization Starter Guide by
25. The Anatomy of a Large-Scale Hypertextual Web Search
Engine By Sergey Brin and Lawrence Page
A Comparative Analysis of Web Page Ranking Algorithms by
Dilip Kumar Sharma. / (IJCSE) International Journal on Computer
Science and Engineering.