Pagerank CS2HS Workshop. Google Google’s Pagerank algorithm is a marvel in terms of its...
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![Page 1: Pagerank CS2HS Workshop. Google Google’s Pagerank algorithm is a marvel in terms of its effectiveness and simplicity. The first company whose initial.](https://reader030.fdocuments.net/reader030/viewer/2022032702/56649caf5503460f949723ae/html5/thumbnails/1.jpg)
Pagerank
CS2HS Workshop
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• Google’s Pagerank algorithm is a marvel in terms of its effectiveness and simplicity.
• The first company whose initial success was entirely due to “discovery/invention” of a clever algorithm.
• The key idea by Larry Page and Sergey Brin was presented in 1998 at the WWW conference in Brisbane, Queensland.
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Outline
• Two parts:1. Random Surfer Model (RSM) – the
conceptual basis of pagerank.
2. Expressing RSM as a problem of eigen-decomposition.
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The Key Ideas of Pagerank
• The Pagerank, at least initially, was based on three key “tricks”
1. The hyperlink trick2. The authority trick3. The random-surfer model
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Hyperlink trick
• A hyperlink is pointer embedded inside a web page which leads to another page.
• Hyperlink trick: the importance of a page A can be measured by the number of pages pointing to A
Alan Turing is father of
CS
Alan Turing was born in
the UK in 1912
UK is a small island of the
coast of France
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Hyperlink example
• The importance of A is 2• The importance of E is 3
• Computers are bad in understanding the content of pages but good at counting
• Importance based just on the count of hyperlinks can be easily exploited
A
B
D
C
E
F
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Authority Trick
• All links are not equal !
CS is a relatively
new discipline
An investment in CS will solve trade deficit
Hi, I am Sanjay from
Sydney
Hi, I am Julia Gillard, PM of
Australia…
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Authority Example
• Authority Count: Cascade the number of counts
A
B C
2
1 1
D
EF2
5
3
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Authority Example…cont
• Presence of cycles will immediately make the authoritative counts redundant !
D
EF2
5
3
D
EF2
?
8
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Random Surfer Model
• A surfer browsing the web by randomly following links, occasionally jumping to a random page
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Random Surfer Model
• Combines hyperlink trick, authority trick and solves the cycle problem ! Why ?
• Score or Rank of page A is the proportion of time a random surfer will land up on A
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Mathematical Modeling
• Three steps:
1. Model the web as a graph.2. Convert the graph into a matrix A3. Compute the eigenvector of A
corresponding to eigenvalue 1.
Pagerank: The components of the eigenvector
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A graph and a matrix
• A graph is a mathematical structure which consists of vertices and edges
a
b
c
d e
Link matrix
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Matrices
• In middle school we learn how to solve simple equations of the form.
• In general, solve equations of the form Ax = b
Ax = b
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Special form of Ax=b
• An important special case of Ax = b is the equation of the form
• Ax = λx
• λ is called the eigenvalue and the resulting x is called the eigenvector corresponding to λ
• This is one of the most fundamental decomposition in all of mathematics – no kidding!
• Newton, Heisenberg, Schrodinger, climate change, stock market, environmental science, aircraft design,…….
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Pagerank
• The pagerank vector is the solution of the equation:
• Ap = p (thus λ = 1)
• Where A is related to the link matrix
• Note size of A: number or pages on the web –in the billions
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Pagerank Equation
• Let p be the page rank vector and L be the link matrix.
• Here r is the random restart probability (set to 0.15 by Page and Brin)
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Pagerank…cont
• Let e by the vector of 1’s: e = (1,1,….1)
• Let average pagerank be 1, i.e.,
• Let
• Roll the drums………
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The final page rank equation
One line code: Open Matlab and type: [u,v]=eig(A); read of the ranks from the eigenvector corresponding to eigenvalue 1
Lab: Create your web with six pages (with your link structure) and calculate the pagerank.Experiment with different links and confirm if the resulting ranks capture: hyperlink trick,Authority trick and solve the cycle problem