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- Romil Jain [email protected]

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Introduction to Google PageRank Algorithm. - Romil Jain [email protected] World Wide Web. WWW is HUGE. Approximate estimations [1]: ~50 million active web sites ~25 billion web pages ~1 billion users. There are a large number of search engines too [2]:

Related searches for world wide web

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World Wide Web

- WWW is HUGE. Approximate estimations [1]:
- ~50 million active web sites
- ~25 billion web pages
- ~1 billion users

- There are a large number of search engines too [2]:
- At least 3,105 search engines

Ranking

Module

Query

Module

Indexing

Module

Page

Repository

Indexes

Results

Anatomy of a Search EngineUser Query

WWW

Ranking Module

- Key is to find those pages that the user desires
- Takes a set of relevant web pages and ranks them
- Rank is generally a function of:
- Content Score &
- Popularity Score (The focus of this talk)
- E.g. “What are some good Indian restaurants in Toronto?”

r(Pj)

r(Pi) : PageRank of page Pi

Bi : set of pages pointing to Pi

| Pj | : # out-links from Pj

r(Pi) =

|Pj|

Pj Bi

Ranking Web Pages by Popularity- PageRank algorithm, given by Sergey Brin and Larry Page in 1998 [1]
- Exploits the linked structure of the web for computing popularity

k : kth iteration

rk(Pj)

rk+1(Pi) =

|Pj|

Pj Bi

Ranking by Popularity (cont’d)r(Pj)

- But r(Pj) are unknown !
- So use and iterative procedure:

r(Pi) =

|Pj|

Pj Bi

- r0(Pj) = 1/n, where n is # web pages

P1

P2

P3

P4

P5

P6

P1

0

1/2

1/2

0

0

0

P2

0

0

0

0

0

0

Hyperlink Matrix H =

P3

1/3

1/3

0

0

1/3

0

P4

0

0

0

0

1/2

1/2

P5

0

0

0

1/2

0

1/2

P6

0

0

0

1

0

0

rk(Pj)

rk+1(Pi) =

|Pj|

Pj Bi

r0(Pj) = 1/n

Matrix Notation1

2

3

6

5

4

(k+1)T = (k)TH,

(k)T : PageRank vector after kth iteration

(0)T : 1/n eT

(k+1)T = (k)T H

Nice (?) Properties of H- Sparse n n matrix

- Less storage space (25 billion web pages!)

- Each iteration requires (nnz(H)) computations. H has about 10n nonzero. So (n) computations.
- Note that a dense matrix would require (n2) computation

- The dangling nodescreate 0 rows in H. All other rows have sum = 1. Thus H is substochastic matrix

(k+1)T = (k)T H

1

2

with (0)T = (1 0),(k)T will flip-flop between (1 0) and (0 1) !

Issues with Iterative Process- Will it converge or continue indefinitely?

- What properties of Hwill ensure convergence?

- Does convergence depend on (0)T ?

- How long will it take to converge i.e. what k is the fixed point?

- Does a converged T give useful page ranks?

All these questions can be answered using theory of

Markov Chains & Stochastic Matrices…

Stochastic Matrix

A stochastic matrix S is:

- n n matrix with each row-sum = 1
- for each sij ,0 sij1

Markov Chain for a Random Surfer

Transition Probability Matrix

Power of Stochastic Matrix

If we start from C, what is the probability that we will reach B in 2 steps?

P(CB2) = P(CA)P(AB) + P(CB)P(BB) + P(CC)P(CB)

It can be proven for a stochastic

matrix S that:

lim Sn = S* , if 0 sij 1

n

Power ConvergenceIn 3, 4, 5, 6, 7 steps?

State Vector Transition

If xTis a stochastic probability distribution vector of a given state, then:

x (k+1)T= x (k)TS

Similar to (k+1)T = (k)T H, except that His not stochastic!

x (n+1)T = x (n)T S

State Vector ConvergenceIf we start with x(0)T, then

lim x(n)T = x (0)T lim Sn = x (0)TS* = x*

n n

P1

P2

P3

P4

P5

P6

P1

0

1/2

1/2

0

0

0

P2

0

0

0

0

0

0

Hyperlink Matrix H =

P3

1/3

1/3

0

0

1/3

0

P4

0

0

0

0

1/2

1/2

P5

0

0

0

1/2

0

1/2

P6

0

0

0

1

0

0

The problem is due to these dangling rows

H is not stochastic!(k+1)T = (k)T H

P1

P2

P3

P4

P5

P6

P1

0

1/2

1/2

0

0

0

P2

1/6

1/6

1/6

1/6

1/6

1/6

P3

1/3

1/3

0

0

1/3

0

P4

0

0

0

0

1/2

1/2

P5

0

0

0

1/2

0

1/2

P6

0

0

0

1

0

0

Dangling rows

eliminated…

S =

Adjustment 1 to HA random surfer can randomly “jump” to any page after he encounters a dangling node

S = H + a(1/n eT)

a is called the dangling node vector. ai = 1 if page i is dangling otherwise 0.

G = S + (1 - ) E , 0 1

E = 1/n eeT is called the teleportation matrix

is the % of time a user surfs or teleports

G is called the Google Matrix

(k+1)T = (k)T S

Adjustment 2 to H0 sij 1 not true for S!

A random surfer can randomly “teleport” to any page irrespective of the current page.

Finally we have G!

G = S + (1 - ) E , 0 1

(k+1)T = (k)T G

- Gis stochastic
- 0 gij 1 true for G

Therefore the above equation converges for any (0)T

But now G is no longer sparse . In fact it is completely dense!

(k+1)T = (k)T G

Fortunately…G = S + (1 - ) E

= S + (1 - ) 1/n eeT

= (H + 1/n aeT) + (1 - ) 1/n eeT

= H + (a + (1 - ) e) 1/n eT

Therefore:

(k+1)T = (k)T G

= (k)T H + ( (k)T a + (1 - ) (k)T e ) 1/n eT

= (k)T H + ( (k)T a + (1 - )) 1/n eT (?)

Now vector multiplications are done on extremely sparse H

(k+1)T = (k)T G

Importance of G = S + (1 - ) E , 0 1

(k+1)T = (k)T G

What must be chosen?

It can be shown that rate of convergence is the rate at which

k 0

0, T converges immediately, but completely unrealistic!

1, Tmay never converge, again unrealistic !

We want to be as close as possible to 1

(k+1)T = (k)T G

= 0.85 Saves the DayG = S + (1 - ) E, 0 1

Brin & Page initially chose = 0.85, and this is still the value

used by Google

Takes about 50 iterations (3 days) to converge sufficiently

Accuracy is 50= .8550 .000296, which is sufficient for

Google’s needs

(k+1)T = (k)T G

Importance of Teleportation Matrix EG = S + (1 - ) E

Initially we had E = 1/n eeT

This means that a random surfer can teleport to any web page with equal probability 1/n

Instead of 1/n eeT use evT , where vTis the

personalization or teleportation vector.

vT is used to counter-act link farms (like SearchKing.com)

(k+1)T = (k)T G

Issue: Sensitivity of PageRankIt can be shown that:

1

d (k)T

d

1 -

as 1, 1/(1- )

So, PageRank is quite sensitive to small changes in the web.

Google computes PageRank from scratch every month!

Can we compute i+1 from i without computing i+1 from scratch?

(k+1)T = (k)T G

Issue: PageRank is Query Independent!- PageRank is pre-computed.
- It means that to be better linked is more important than to contain the search terms
- This is significant because a badly linked page, might be popular within the community of pages with the same topic

A rosy idea: Is it feasible to compute PageRank after the relevant

documents have been retrieved?

(k+1)T = (k)T G

Issue: PageRank is Dead!Not for now, but is susceptible to a lot of damage:

- PageRank is based upon an ideal democratic structure of the web
- But hackers, spammers and SEO’s know too much about Google to skew the rankings
- Typical examples are Link Farms and Google Bombs.
- Bloggers created a bomb where if you typed “miserable failure” then Google would take you to www.whitehouse.gov!

How can we detect and fight Rank Skewing?

References

- The size of the World Wide Web, May 2007.
http://www.pandia.com/sew/383-web-size.html.

- Search Engines Worldwide, Jan 2003. http://home.inter.net/takakuwa/search/search.html .
- Langville and Meyer. Google’s PageRank and Beyond. Princeton University Press, 2006.
- Brin and Page. The Anatomy of a Large-scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 1998.

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