Download p338a - CSE, IIT Bombay

The Structure of Broad Topics
on the Web
Soumen Chakrabarti
Mukul M. Joshi
Kunal Punera
(IIT Bombay)
David M. Pennock
(NEC Research Institute)
Graph structure of the Web
 Over two billion nodes, two trillion links
 Power-law degree distribution
• Pr(degree = k)  1/k2.1
 Looks like a “bow-tie” at large scale
IN
Strongly
connected
core (SCC)
OUT
“This is
the Web”
The need for content-based models
 Why does a radius-1
expansion help in
topic distillation?
Query
Search
engine
Root
set
Crawler
 Why does topicspecific focused
crawling work?
Classifier
Check
Prune
frontier topic if irrelevant
 Why is a global
d
p(u )
PageRank useful for p(v)  N  (1  d ) OutDegree(u )
u v
specific queries?

Uniform
jump
Walk to
out-neighbor
The need for content-based models
 How are different topics linked to each other?
 Are topic directories representative of Web
topic populations?
 Are standard collections (e.g., TREC W10G)
representative of Web topics?
“This is
the Web
with topics”
How to characterize “topics”
 Web directories—most natural choice
 Started with http://dmoz.org
 Keep pruning until all leaf topics Test doc
have enough (>300) samples
Classifier
 Approx 120k sample URLs
Topic
Prob
Arts
0.1
 Flatten to approx 482 topics
Computers
0.3
0.6
 Train text classifier (Rainbow) Science
 Characterize new document d as a
vector of probabilities pd = (Pr(c|d) c)
Critique and defense
 Cannot capture fine-grained or
emerging topics
• Emerging topics most often specialize
existing broad topics
• Broad topics rarely change
 Classifier may be inaccurate
• Adequate if much better than random
guessing of topic label
• Can compensate errors using held-out
validation data
Background topic distribution
 What fraction of Web pages
are about Health?
 Sampling via random walk
• PageRank walk (Henzinger et al.)
• Undirected regular walk (BarYossef et al.)
 Make graph undirected
 Add self-loops so that all nodes
have the same degree
 Sample with large stride
 Collect topic histograms
Convergence
Stride=30k
Stride=75k
Distribution
difference
0.4
Background distribution
0.3
0.2
0.1
 Start from pairs of diverse topics
 Two random walks, sample from each walk
 Measure distance between topic distributions
• L1 distance |p1 – p2| = c|p1(c) – p2(c)| in [0,2]
• Below .05 —.2 within 300—400 physical pages
Sports
Society
Shopping
Science
Reference
Recreation
Home
Health
1000
Games
500
Computers
Arts
0
Business
1
0.8
0.6
0.4
0.2
0
#hops
0
Biases in topic directories
 Use Dmoz to train a
classifier
 Sample the Web
 Classify samples
 Diff Dmoz topic
distribution from Web
sample topic distribution
 Report maximum
deviation in fractions
 NOTE: Not exactly Dmoz
Dmoz over-represents
Games.Video_Games
Society.People
Arts.Celebrities
...Education.Colleges
...Travel.Reservations
Dmoz under-represents
…WWW…Directories!
Sports.Hockey
Society.Philosophy
Education…K12…
Recreation…Camping
Topic-specific degree distribution
 Preferential
attachment:
connect u to v w.p.
proportional to the
degree of v,
regardless of topic
 More realistic: u has
a topic, and links to v
with related topics
 Unclear if power-law
should be upheld
Intra-topic
linkage
Inter-topic
linkage
Random forward walk without jumps
/Arts/Music
/Sports/Soccer
1.4
1
1.2
0.8
0.6
L_1 Distance
L_1 Distance
1.2
1
0.8
From background
From hop0
0.4
0.2
0.6
From background
From hop0
0.4
0
5
10
15
Wander hops
20
0
5
10
15
Wander hops
 Sampling walk is designed to mix topics well
 How about walking forward without jumping?
• Start from a page u0 on a specific topic
• Forward random walk (u0, u1, …, ui, …)
• Compare (Pr(c|ui) c) with (Pr(c|u0) c) and with
the background distribution
20
Observations and implications
 Forward walks wander away from
starting topic slowly
 But do not converge to the
background distribution
 Global PageRank ok also
for topic-specific queries
• Jump parameter d=.1—.2
• Topic drift not too bad within
path length of 5—10
• Prestige conferred mostly by
same-topic neighbors
W.p. d jump to
a random node
W.p. (1-d)
jump to an
out-neighbor
u.a.r.
Jump
 Also explains why focused crawling works
Highprestige
node
Citation matrix
 Given a page is about topic i, how likely
is it to link to topic j?
• Matrix C[i,j] = probability that page about
topic i links to page about topic j
• Soft counting: C[i,j] += Pr(i|u)Pr(j|v) u
 Applications
• Classifying Web pages into topics
• Focused crawling for topic-specific pages
• Finding relations between topics in a
directory
v
Citation, confusion, correction
From topic
Arts
Business
Computers
Games
Health
Home
Recreation
Reference
Science
Shopping
Society
Sports
To topic 
Classifier’s confusion
on held-out documents
can be used to correct
confusion matrix
From topic
To topic 
Guessed topic 
True topic
Fine-grained views of citation
Prominent off-diagonal
entries raise design
issues for taxonomy
editors and maintainers
Clear block-structure derived
from coarse-grain topics
Strong diagonals reflect
tightly-knit topic communities
Concluding remarks
 A model for content-based communities
• New characterization and measurement of
topical locality on the Web
• How to set the PageRank jump parameter?
• Topical stability of topic distillation
• Better crawling and classification
 A tool for Web directory maintenance
• Fair sampling and representation of topics
• Block-structure and off-diagonals
• Taxonomy inversion