Anchor text Citation analysis PageRank HITS: Hubs & Authorities
PV211: Introduction to Information Retrieval
https://www.fi.muni.cz/~sojka/PV211
IIR 21: Link analysis
Handout version
Petr Sojka, Hinrich Schütze et al.
Faculty of Informatics, Masaryk University, Brno
Center for Information and Language Processing, University of Munich
2023-05-10
(compiled on 2023-03-22 18:16:36)
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Overview
1 Anchor text
2 Citation analysis
3 PageRank
4 HITS: Hubs & Authorities
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Take-away today
Anchor text: What exactly are links on the web and why are
they important for IR?
Citation analysis: the mathematical foundation of PageRank
and link-based ranking
PageRank: the original algorithm that was used for link-based
ranking on the web
Hubs & Authorities: an alternative link-based ranking
algorithm
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
The web as a directed graph
page d1 anchor text page d2
hyperlink
Assumption 1: A hyperlink is a quality signal.
The hyperlink d1 → d2 indicates that d1’s author deems d2
high-quality and relevant.
Assumption 2: The anchor text describes the content of d2.
We use anchor text somewhat loosely here for: the text
surrounding the hyperlink.
Example: “You can find cheap cars <a
href=http://...>here</a>.”
Anchor text: “You can find cheap cars here”
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
[text of d2] only vs. [text of d2] + [anchor text → d2]
Searching on [text of d2] + [anchor text → d2] is often more
effective than searching on [text of d2] only.
Example: Query IBM
Matches IBM’s copyright page
Matches many spam pages
Matches IBM Wikipedia article
May not match IBM home page!
. . . if IBM home page is mostly graphics
Searching on [anchor text → d2] is better for the query IBM.
In this representation, the page with the most occurrences of
IBM is www.ibm.com.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Anchor text containing IBM pointing to www.ibm.com
www.nytimes.com: “IBM acquires Webify”
www.slashdot.org: “New IBM optical chip”
www.stanford.edu: “IBM faculty award recipients”
www.ibm.com
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Indexing anchor text
Thus: Anchor text is often a better description of a page’s
content than the page itself.
Anchor text can be weighted more highly than document text.
(based on Assumptions 1&2)
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Exercise: Assumptions underlying PageRank
Assumption 1: A link on the web is a quality signal – the
author of the link thinks that the linked-to page is high-quality.
Assumption 2: The anchor text describes the content of the
linked-to page.
Is assumption 1 true in general?
Is assumption 2 true in general?
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Google bombs
A Google bomb is a search with “bad” results due to
maliciously manipulated anchor text.
Google introduced a new weighting function in 2007 that fixed
many Google bombs.
Still some remnants: [dangerous cult] on Google, Bing, Yahoo
Coordinated link creation by those who dislike the Church of
Scientology
Defused Google bombs: [dumb motherf. . . ], [who is a
failure?], [evil empire]
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Origins of PageRank: Citation analysis (1)
Citation analysis: analysis of citations in the scientific
literature
Example citation: “Miller (2001) has shown that physical
activity alters the metabolism of estrogens.”
We can view “Miller (2001)” as a hyperlink linking two
scientific articles.
One application of these “hyperlinks” in the scientific
literature:
Measure the similarity of two articles by the overlap of other
articles citing them.
This is called cocitation similarity.
Cocitation similarity on the web: Google’s “related:” operator,
e.g. [related:www.ford.com]
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Origins of PageRank: Citation analysis (2)
Another application: Citation frequency can be used to
measure the impact of a scientific article.
Simplest measure: Each citation gets one vote.
On the web: citation frequency = inlink count
However: A high inlink count does not necessarily mean high
quality . . .
. . . mainly because of link spam.
Better measure: weighted citation frequency or citation rank
An citation’s vote is weighted according to its citation impact.
Circular? No: can be formalized in a well-defined way.
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Origins of PageRank: Citation analysis (3)
Better measure: weighted citation frequency or citation rank
This is basically PageRank.
PageRank was invented in the context of citation analysis by
Pinsker and Narin in the 1960s.
Citation analysis is a big deal: The budget and salary of this
lecturer are / will be determined by the impact of his
publications!
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Origins of PageRank: Summary
We can use the same formal representation for
citations in the scientific literature
hyperlinks on the web
Appropriately weighted citation frequency is an excellent
measure of quality . . .
. . . both for web pages and for scientific publications.
Next: PageRank algorithm for computing weighted citation
frequency on the web
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Model behind PageRank: Random walk
Imagine a web surfer doing a random walk on the web
Start at a random page
At each step, go out of the current page along one of the links
on that page, equiprobably
In the steady state, each page has a long-term visit rate.
This long-term visit rate is the page’s PageRank.
PageRank = long-term visit rate = steady state probability
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Formalization of random walk: Markov chains
A Markov chain consists of N states, plus an N × N transition
probability matrix P.
state = page
At each step, we are on exactly one of the pages.
For 1 ≤ i, j ≤ N, the matrix entry Pij tells us the probability
of j being the next page, given we are currently on page i.
Clearly, for all i, N
j=1 Pij = 1
di dj
Pij
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Example web graph
d0
d2 d1
d5
d3 d6
d4
car benz
ford
gm
honda
jaguar
jag
cat
leopard
tiger
jaguar
lion
cheetah
speed
PageRank
d0 0.05 d1 0.04 d2 0.11
d3 0.25 d4 0.21 d5 0.04
d6 0.31
PageRank(d2)< PageRank(d6):
why?
a h
d0 0.10 0.03
d1 0.01 0.04
d2 0.12 0.33
d3 0.47 0.18
d4 0.16 0.04
d5 0.01 0.04
d6 0.13 0.35
highest in-degree: d2, d3, d6
highest out-degree: d2, d6
highest PageRank: d6
highest hub score: d6 (close: d2)
highest authority score: d3
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Link matrix for example
d0 d1 d2 d3 d4 d5 d6
d0 0 0 1 0 0 0 0
d1 0 1 1 0 0 0 0
d2 1 0 1 1 0 0 0
d3 0 0 0 1 1 0 0
d4 0 0 0 0 0 0 1
d5 0 0 0 0 0 1 1
d6 0 0 0 1 1 0 1
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Transition probability matrix P for example
d0 d1 d2 d3 d4 d5 d6
d0 0.00 0.00 1.00 0.00 0.00 0.00 0.00
d1 0.00 0.50 0.50 0.00 0.00 0.00 0.00
d2 0.33 0.00 0.33 0.33 0.00 0.00 0.00
d3 0.00 0.00 0.00 0.50 0.50 0.00 0.00
d4 0.00 0.00 0.00 0.00 0.00 0.00 1.00
d5 0.00 0.00 0.00 0.00 0.00 0.50 0.50
d6 0.00 0.00 0.00 0.33 0.33 0.00 0.33
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Long-term visit rate
Recall: PageRank = long-term visit rate
Long-term visit rate of page d is the probability that a web
surfer is at page d at a given point in time.
Next: what properties must hold of the web graph for the
long-term visit rate to be well defined?
The web graph must correspond to an ergodic Markov chain.
First a special case: The web graph must not contain dead
ends.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Dead ends
??
The web is full of dead ends.
Random walk can get stuck in dead ends.
If there are dead ends, long-term visit rates are not
well-defined (or non-sensical).
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Teleporting – to get us out of dead ends
At a dead end, jump to a random web page with prob. 1/N.
At a non-dead end, with probability 10%, jump to a random
web page (to each with a probability of 0.1/N).
With remaining probability (90%), go out on a random
hyperlink.
For example, if the page has 4 outgoing links: randomly
choose one with probability (1-0.10)/4=0.225
10% is a parameter, the teleportation rate.
Note: “jumping” from dead end is independent of
teleportation rate.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Result of teleporting
With teleporting, we cannot get stuck in a dead end.
But even without dead ends, a graph may not have
well-defined long-term visit rates.
More generally, we require that the Markov chain be
ergodic.
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Ergodic Markov chains
A Markov chain is ergodic iff it is irreducible and aperiodic.
Irreducibility. Roughly: there is a path from any page to any
other page.
Aperiodicity. Roughly: The pages cannot be partitioned such
that the random walker visits the partitions sequentially.
A non-ergodic Markov chain:
1.0
1.0
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Ergodic Markov chains
Theorem: For any ergodic Markov chain, there is a unique
long-term visit rate for each state.
This is the steady-state probability distribution.
Over a long time period, we visit each state in proportion to
this rate.
It doesn’t matter where we start.
Teleporting makes the web graph ergodic.
⇒ Web-graph+teleporting has a steady-state probability
distribution.
⇒ Each page in the web-graph+teleporting has a
PageRank.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Where we are
We now know what to do to make sure we have a well-defined
PageRank for each page.
Next: how to compute PageRank
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Formalization of “visit”: Probability vector
A probability (row) vector x = (x1, . . . , xN) tells us where the
random walk is at any point.
Example:
( 0 0 0 . . . 1 . . . 0 0 0 )
1 2 3 . . . i . . . N-2 N-1 N
More generally: the random walk is on page i with probability
xi .
Example:
( 0.05 0.01 0.0 . . . 0.2 . . . 0.01 0.05 0.03 )
1 2 3 . . . i . . . N-2 N-1 N
xi = 1
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Change in probability vector
If the probability vector is x = (x1, . . . , xN) at this step, what
is it at the next step?
Recall that row i of the transition probability matrix P tells us
where we go next from state i.
So from x, our next state is distributed as xP.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Steady state in vector notation
The steady state in vector notation is simply a vector
π = (π1, π2, . . . , πN) of probabilities.
(We use π to distinguish it from the notation for the
probability vector x.)
πi is the long-term visit rate (or PageRank) of page i.
So we can think of PageRank as a very long vector – one
entry per page.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Steady-state distribution: Example
What is the PageRank / steady state in this example?
d1 d2
0.75
0.25
0.25
0.75
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Steady-state distribution: Example
x1 x2
Pt(d1) Pt(d2)
P11 = 0.25 P12 = 0.75
P21 = 0.25 P22 = 0.75
t0 0.25 0.75 0.25 0.75
t1 0.25 0.75 (convergence)
PageRank vector = π = (π1, π2) = (0.25, 0.75)
Pt(d1) = Pt−1(d1) ∗ P11 + Pt−1(d2) ∗ P21
Pt(d2) = Pt−1(d1) ∗ P12 + Pt−1(d2) ∗ P22
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How do we compute the steady state vector?
In other words: how do we compute PageRank?
Recall: π = (π1, π2, . . . , πN) is the PageRank vector, the
vector of steady-state probabilities . . .
. . . and if the distribution in this step is x, then the
distribution in the next step is xP.
But π is the steady state!
So: π = πP
Solving this matrix equation gives us π.
π is the principal left eigenvector for P . . .
. . . that is, π is the left eigenvector with the largest eigenvalue.
All transition probability matrices have largest eigenvalue 1.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
One way of computing the PageRank π
Start with any distribution x, e.g., uniform distribution
After one step, we’re at xP.
After two steps, we’re at xP2.
After k steps, we’re at xPk.
Algorithm: multiply x by increasing powers of P until
convergence.
This is called the power method.
Recall: regardless of where we start, we eventually reach the
steady state π.
Thus: we will eventually (in asymptotia) reach the steady
state.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Power method: Example
What is the PageRank / steady state in this example?
d1 d2
0.9
0.3
0.1
0.7
The steady state distribution (= the PageRanks) in this
example are 0.25 for d1 and 0.75 for d2.
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Computing PageRank: Power method
x1 x2
Pt(d1) Pt(d2)
P11 = 0.1 P12 = 0.9
P21 = 0.3 P22 = 0.7
t0 0 1 0.3 0.7 = xP
t1 0.3 0.7 0.24 0.76 = xP2
t2 0.24 0.76 0.252 0.748 = xP3
t3 0.252 0.748 0.2496 0.7504 = xP4
. . . . . .
t∞ 0.25 0.75 0.25 0.75 = xP∞
PageRank vector = π = (π1, π2) = (0.25, 0.75)
Pt(d1) = Pt−1(d1) ∗ P11 + Pt−1(d2) ∗ P21
Pt(d2) = Pt−1(d1) ∗ P12 + Pt−1(d2) ∗ P22
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Power method: Example
What is the PageRank / steady state in this example?
d1 d2
0.9
0.3
0.1
0.7
The steady state distribution (= the PageRanks) in this
example are 0.25 for d1 and 0.75 for d2.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Exercise: Compute PageRank using power method
d1 d2
0.3
0.2
0.7
0.8
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Solution
x1 x2
Pt(d1) Pt(d2)
P11 = 0.7 P12 = 0.3
P21 = 0.2 P22 = 0.8
t0 0 1 0.2 0.8
t1 0.2 0.8 0.3 0.7
t2 0.3 0.7 0.35 0.65
t3 0.35 0.65 0.375 0.625
. . .
t∞ 0.4 0.6 0.4 0.6
PageRank vector = π = (π1, π2) = (0.4, 0.6)
Pt(d1) = Pt−1(d1) ∗ P11 + Pt−1(d2) ∗ P21
Pt(d2) = Pt−1(d1) ∗ P12 + Pt−1(d2) ∗ P22
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PageRank summary
Preprocessing
Given graph of links, build matrix P
Apply teleportation
From modified matrix, compute π
πi is the PageRank of page i.
Query processing
Retrieve pages satisfying the query
Rank them by their PageRank
Return reranked list to the user
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PageRank issues
Real surfers are not random surfers.
Examples of nonrandom surfing: back button, short vs. long
paths, bookmarks, directories – and search!
→ Markov model is not a good model of surfing.
But it’s good enough as a model for our purposes.
Simple PageRank ranking (as described on previous slide)
produces bad results for many pages.
Consider the query [video service]
The Yahoo home page (i) has a very high PageRank and (ii)
contains both video and service.
If we rank all Boolean hits according to PageRank, then the
Yahoo home page would be top-ranked.
Clearly not desirable
In practice: rank according to weighted combination of raw
text match, anchor text match, PageRank & other factors
→ see lecture on Learning to Rank
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Example web graph
d0
d2 d1
d5
d3 d6
d4
car benz
ford
gm
honda
jaguar
jag
cat
leopard
tiger
jaguar
lion
cheetah
speed
PageRank
d0 0.05 d1 0.04 d2 0.11
d3 0.25 d4 0.21 d5 0.04
d6 0.31
PageRank(d2)< PageRank(d6):
why?
a h
d0 0.10 0.03
d1 0.01 0.04
d2 0.12 0.33
d3 0.47 0.18
d4 0.16 0.04
d5 0.01 0.04
d6 0.13 0.35
highest in-degree: d2, d3, d6
highest out-degree: d2, d6
highest PageRank: d6
highest hub score: d6 (close: d2)
highest authority score: d3
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Transition (probability) matrix
d0 d1 d2 d3 d4 d5 d6
d0 0.00 0.00 1.00 0.00 0.00 0.00 0.00
d1 0.00 0.50 0.50 0.00 0.00 0.00 0.00
d2 0.33 0.00 0.33 0.33 0.00 0.00 0.00
d3 0.00 0.00 0.00 0.50 0.50 0.00 0.00
d4 0.00 0.00 0.00 0.00 0.00 0.00 1.00
d5 0.00 0.00 0.00 0.00 0.00 0.50 0.50
d6 0.00 0.00 0.00 0.33 0.33 0.00 0.33
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Transition matrix with teleporting, teleportation rate=0.14
d0 d1 d2 d3 d4 d5 d6
d0 0.02 0.02 0.88 0.02 0.02 0.02 0.02
d1 0.02 0.45 0.45 0.02 0.02 0.02 0.02
d2 0.31 0.02 0.31 0.31 0.02 0.02 0.02
d3 0.02 0.02 0.02 0.45 0.45 0.02 0.02
d4 0.02 0.02 0.02 0.02 0.02 0.02 0.88
d5 0.02 0.02 0.02 0.02 0.02 0.45 0.45
d6 0.02 0.02 0.02 0.31 0.31 0.02 0.31
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Power method vectors xPk
x xP1 xP2 xP3 xP4 xP5 xP6 xP7 xP8 xP9 xP10 xP11 xP12 xP13
d0 0.14 0.06 0.09 0.07 0.07 0.06 0.06 0.06 0.06 0.05 0.05 0.05 0.05 0.05
d1 0.14 0.08 0.06 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
d2 0.14 0.25 0.18 0.17 0.15 0.14 0.13 0.12 0.12 0.12 0.12 0.11 0.11 0.11
d3 0.14 0.16 0.23 0.24 0.24 0.24 0.24 0.25 0.25 0.25 0.25 0.25 0.25 0.25
d4 0.14 0.12 0.16 0.19 0.19 0.20 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21
d5 0.14 0.08 0.06 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
d6 0.14 0.25 0.23 0.25 0.27 0.28 0.29 0.29 0.30 0.30 0.30 0.30 0.31 0.31
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Example web graph
d0
d2 d1
d5
d3 d6
d4
car benz
ford
gm
honda
jaguar
jag
cat
leopard
tiger
jaguar
lion
cheetah
speed
PageRank
d0 0.05 d1 0.04 d2 0.11
d3 0.25 d4 0.21 d5 0.04
d6 0.31
PageRank(d2)< PageRank(d6):
why?
a h
d0 0.10 0.03
d1 0.01 0.04
d2 0.12 0.33
d3 0.47 0.18
d4 0.16 0.04
d5 0.01 0.04
d6 0.13 0.35
highest in-degree: d2, d3, d6
highest out-degree: d2, d6
highest PageRank: d6
highest hub score: d6 (close: d2)
highest authority score: d3
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How important is PageRank?
Frequent claim: PageRank is the most important component
of web ranking.
The reality:
There are several components that are at least as important:
e.g., anchor text, phrases, proximity, tiered indexes . . .
Rumor has it that PageRank in its original form (as presented
here) now has a negligible impact on ranking!
However, variants of a page’s PageRank are still an essential
part of ranking.
Adressing link spam is difficult and crucial.
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HITS – Hyperlink-Induced Topic Search
Premise: there are two different types of relevance on the web.
Relevance type 1: Hubs. A hub page is a good list of [links to
pages answering the information need].
E.g., for query [chicago bulls]: Bob’s list of recommended
resources on the Chicago Bulls sports team
Relevance type 2: Authorities. An authority page is a direct
answer to the information need.
The home page of the Chicago Bulls sports team
By definition: Links to authority pages occur repeatedly on
hub pages.
Most approaches to search (including PageRank ranking)
don’t make the distinction between these two very different
types of relevance.
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Hubs and authorities: Definition
A good hub page for a topic links to many authority pages for
that topic.
A good authority page for a topic is linked to by many hub
pages for that topic.
Circular definition – we will turn this into an iterative
computation.
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
Example for hubs and authorities
hubs authorities
www.bestfares.com
www.airlinesquality.com
blogs.usatoday.com/sky
aviationblog.dallasnews.com
www.aa.com
www.delta.com
www.united.com
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Anchor text Citation analysis PageRank HITS: Hubs & Authorities
How to compute hub and authority scores
Do a regular web search first
Call the search result the root set
Find all pages that are linked to or link to pages in the root set
Call this larger set the base set
Finally, compute hubs and authorities for the base set (which
we’ll view as a small web graph)
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Root set and base set (1)
base set
root set
1) The root set 2) Nodes that root set nodes link to 3) Nodes
that link to root set nodes 4) The base set
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Root set and base set (2)
Root set typically has 200–1,000 nodes.
Base set may have up to 5,000 nodes.
Computation of base set, as shown on previous slide:
Follow outlinks by parsing the pages in the root set
Find d’s inlinks by searching for all pages containing a link
to d
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Hub and authority scores
Compute for each page d in the base set a hub score h(d) and
an authority score a(d)
Initialization: for all d: h(d) = 1, a(d) = 1
Iteratively update all h(d), a(d)
After convergence:
Output pages with highest h scores as top hubs
Output pages with highest a scores as top authorities
So we output two ranked lists
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Iterative update
For all d: h(d) = d→y a(y)
d
y1
y2
y3
For all d: a(d) = y→d h(y)
d
y1
y2
y3
Iterate these two steps until convergence
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Details
Scaling
To prevent the a() and h() values from getting too big, can
scale down after each iteration
Scaling factor doesn’t really matter.
We care about the relative (as opposed to absolute) values of
the scores.
In most cases, the algorithm converges after a few
iterations.
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Authorities for query [Chicago Bulls]
0.85 www.nba.com/bulls
0.25 www.essex1.com/people/jmiller/bulls.htm
“da Bulls”
0.20 www.nando.net/SportServer/basketball/nba/chi.html
“The Chicago Bulls”
0.15 users.aol.com/rynocub/bulls.htm
“The Chicago Bulls Home Page”
0.13 www.geocities.com/Colosseum/6095
“Chicago Bulls”
(Ben-Shaul et al, WWW8)
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The authority page for [Chicago Bulls]
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Hubs for query [Chicago Bulls]
1.62 www.geocities.com/Colosseum/1778
“Unbelieveabulls!!!!!”
1.24 www.webring.org/cgi-bin/webring?ring=chbulls
“Erin’s Chicago Bulls Page”
0.74 www.geocities.com/Hollywood/Lot/3330/Bulls.html
“Chicago Bulls”
0.52 www.nobull.net/web_position/kw-search-15-M2.htm
“Excite Search Results: bulls”
0.52 www.halcyon.com/wordsltd/bball/bulls.htm
“Chicago Bulls Links”
(Ben-Shaul et al, WWW8)
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A hub page for [Chicago Bulls]
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Hubs & Authorities: Comments
HITS can pull together good pages regardless of page content.
Once the base set is assembled, we only do link analysis, no
text matching.
Pages in the base set often do not contain any of the query
words.
In theory, an English query can retrieve Japanese-language
pages!
If supported by the link structure between English and
Japanese pages
Danger: topic drift – the pages found by following links may
not be related to the original query.
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Proof of convergence
We define an N × N adjacency matrix A. (We called this the
link matrix earlier.)
For 1 ≤ i, j ≤ N, the matrix entry Aij tells us whether there is
a link from page i to page j (Aij = 1) or not (Aij = 0).
Example:
d3
d1 d2
d1 d2 d3
d1 0 1 0
d2 1 1 1
d3 1 0 0
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Write update rules as matrix operations
Define the hub vector h = (h1, . . . , hN) as the vector of hub
scores. hi is the hub score of page di .
Similarly for a, the vector of authority scores
Now we can write h(d) = d→y a(y) as a matrix operation:
h = Aa . . .
. . . and we can write a(d) = y→d h(y) as a = AT h
HITS algorithm in matrix notation:
Compute h = Aa
Compute a = AT
h
Iterate until convergence
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HITS as eigenvector problem
HITS algorithm in matrix notation. Iterate:
Compute h = Aa
Compute a = AT
h
By substitution we get: h = AAT h and a = AT Aa
Thus, h is an eigenvector of AAT and a is an eigenvector of
AT A.
So the HITS algorithm is actually a special case of the power
method and hub and authority scores are eigenvector values.
HITS and PageRank both formalize link analysis as
eigenvector problems.
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Example web graph
d0
d2 d1
d5
d3 d6
d4
car benz
ford
gm
honda
jaguar
jag
cat
leopard
tiger
jaguar
lion
cheetah
speed
PageRank
d0 0.05 d1 0.04 d2 0.11
d3 0.25 d4 0.21 d5 0.04
d6 0.31
PageRank(d2)< PageRank(d6):
why?
a h
d0 0.10 0.03
d1 0.01 0.04
d2 0.12 0.33
d3 0.47 0.18
d4 0.16 0.04
d5 0.01 0.04
d6 0.13 0.35
highest in-degree: d2, d3, d6
highest out-degree: d2, d6
highest PageRank: d6
highest hub score: d6 (close: d2)
highest authority score: d3
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Raw matrix A for HITS
We double-weight links whose anchors contain query word:
d0 d1 d2 d3 d4 d5 d6
d0 0 0 1 0 0 0 0
d1 0 1 1 0 0 0 0
d2 1 0 1 2 0 0 0
d3 0 0 0 1 1 0 0
d4 0 0 0 0 0 0 1
d5 0 0 0 0 0 1 1
d6 0 0 0 2 1 0 1
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Hub vectors h0,hi = 1
di
A · ai, i ≥ 1
h0 h1 h2 h3 h4 h5
d0 0.14 0.06 0.04 0.04 0.03 0.03
d1 0.14 0.08 0.05 0.04 0.04 0.04
d2 0.14 0.28 0.32 0.33 0.33 0.33
d3 0.14 0.14 0.17 0.18 0.18 0.18
d4 0.14 0.06 0.04 0.04 0.04 0.04
d5 0.14 0.08 0.05 0.04 0.04 0.04
d6 0.14 0.30 0.33 0.34 0.35 0.35
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Authority vectors ai = 1
ci
AT
· hi−1, i ≥ 1
a1 a2 a3 a4 a5 a6 a7
d0 0.06 0.09 0.10 0.10 0.10 0.10 0.10
d1 0.06 0.03 0.01 0.01 0.01 0.01 0.01
d2 0.19 0.14 0.13 0.12 0.12 0.12 0.12
d3 0.31 0.43 0.46 0.46 0.46 0.47 0.47
d4 0.13 0.14 0.16 0.16 0.16 0.16 0.16
d5 0.06 0.03 0.02 0.01 0.01 0.01 0.01
d6 0.19 0.14 0.13 0.13 0.13 0.13 0.13
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Example web graph
d0
d2 d1
d5
d3 d6
d4
car benz
ford
gm
honda
jaguar
jag
cat
leopard
tiger
jaguar
lion
cheetah
speed
PageRank
d0 0.05 d1 0.04 d2 0.11
d3 0.25 d4 0.21 d5 0.04
d6 0.31
PageRank(d2)< PageRank(d6):
why?
a h
d0 0.10 0.03
d1 0.01 0.04
d2 0.12 0.33
d3 0.47 0.18
d4 0.16 0.04
d5 0.01 0.04
d6 0.13 0.35
highest in-degree: d2, d3, d6
highest out-degree: d2, d6
highest PageRank: d6
highest hub score: d6 (close: d2)
highest authority score: d3
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PageRank vs. HITS: Discussion
PageRank can be precomputed, HITS has to be computed at
query time.
HITS is too expensive in most application scenarios.
PageRank and HITS make two different design choices
concerning (i) the eigenproblem formalization (ii) the set of
pages to apply the formalization to.
These two are orthogonal.
We could also apply HITS to the entire web and PageRank to
a small base set.
Claim: On the web, a good hub almost always is also a good
authority.
The actual difference between PageRank ranking and HITS
ranking is therefore not as large as one might expect.
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Exercise
Why is a good hub almost always also a good authority?
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Take-away today
Anchor text: What exactly are links on the web and why are
they important for IR?
Citation analysis: the mathematical foundation of PageRank
and link-based ranking
PageRank: the original algorithm that was used for link-based
ranking on the web
Hubs & Authorities: an alternative link-based ranking
algorithm
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Resources
Chapter 21 of IIR
Resources at https://www.fi.muni.cz/~sojka/PV211/
and http://cislmu.org, materials in MU IS and FI MU
library
American Mathematical Society article on PageRank (popular
science style)
Jon Kleinberg’s home page (main person behind HITS)
A Google bomb and its defusing
Google’s official description of PageRank: PageRank reflects
our view of the importance of web pages by considering more
than 500 million variables and 2 billion terms. Pages that we
believe are important pages receive a higher PageRank and are
more likely to appear at the top of the search results.
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