Semi-Supervised Classification of Network Data Using Very Few Labels
Frank Lin and William W. CohenSchool of Computer Science, Carnegie Mellon University
ASONAM 20102010-08-11, Odense, Denmark
Overview
• Preview• MultiRankWalk– Random Walk with Restart– RWR for Classification
• Seed Preference• Experiments• Results• The Question
Preview
• Classification labels are expensive to obtain• Semi-supervised learning (SSL) learns from
labeled and unlabeled data for classification
Preview
[Adamic & Glance 2005]
Preview
• When it comes to network data, what is a general, simple, and effective method that requires very few labels?
• One that researchers could use as a strong baseline when developing more complex and domain-specific methods?
Our Answer:
MultiRankWalk (MRW)&
Label high PageRank nodes first (authoritative seeding)
Preview• MRW (red) vs. a popular method (blue)
accu
racy
# of training labels
Only 1 training label per class!
Preview• The popular method using authoritative seeding (red & green) vs. random seeding (blue)
Same blue line as before
label “authoritative seeds” first
Overview
• Preview• MultiRankWalk– Random Walk with Restart– RWR for Classification
• Seed Preference• Experiments• Results• The Question
Random Walk with Restart
• Imagine a network, and starting at a specific node, you follow the edges randomly.
• But (perhaps you’re afraid of wondering too far) with some probability, you “jump” back to the starting node (restart!).
If you record the number of times you land on each node, what would that distribution
look like?
Random Walk with Restart
What if we start at a
different node?Start node
Random Walk with Restart
• The walk distribution r satisfies a simple equation:
rur dWd )1(
Start node(s)
Transition matrix of the
network
Restart probability
“Keep-going” probability (damping factor)
Equivalent to the well-known
PageRank ranking if all nodes are
start nodes! (u is uniform)
Random Walk with Restart
• Random walk with restart (RWR) can be solved simply and efficiently with an iterative procedure:
1)1( tt dWd rur
Overview
• Preview• MultiRankWalk– Random Walk with Restart– RWR for Classification
• Seed Preference• Experiments• Results• The Question
RWR for Classification
RWR with start nodes being
labeled points in class A
RWR with start nodes being
labeled points in class B
Nodes frequented more by RWR(A) belongs to class A, otherwise they
belong to B
• Simple idea: use RWR for classification
RWR for Classification
We refer to this method as MultiRankWalk: it classifies data with multiple rankings using random walks
Overview
• Preview• MultiRankWalk– Random Walk with Restart– RWR for Classification
• Seed Preference• Experiments• Results• The Question
Seed Preference
• Obtaining labels for data points is expensive• We want to minimize cost for obtaining labels• Observations:– Some labels inherently more useful than others– Some labels easier to obtain than others
Question: “Authoritative” or “popular” nodes in a network are typically easier to obtain labels for. But are these labels also more
useful than others?
Seed Preference
• Consider the task of giving a human expert (or posting jobs on Amazon Mechanical Turk) a list of data points to label
• The list (seeds) can be generated uniformly at random, or we can have a seed preference, according to simple properties of the unlabeled data
• We consider 3 preferences:– Random– Link Count– PageRank
Nodes with highest counts make the list
Nodes with highest scores make the list
Overview
• Preview• MultiRankWalk– Random Walk with Restart– RWR for Classification
• Seed Preference• Experiments• Results• The Question
Experiments
• Test effectiveness of MRW and compare seed preferences on five real network datasets:
Political Blogs (Liberal vs. Conservative)
Citation Networks (7 and 6 academic fields,
respectively)
Experiments
• We compare MRW against a currently very popular network SSL method – wvRN
“weighted-voted relational
network classifier”
You may know wvRN as the harmonic functions method,
adsorption, random walk with sink nodes, …
Recommended as a strong network SSL baseline in (Macskassy & Provost 2007)
Experiments• To simulate a human expert labeling data, we use the
“ranked-at-least-n-per-class” method
Political blog example with n=2:blogsforbush.comdailykos.commoorewatch.comright-thinking.comtalkingpointsmemo.cominstapundit.commichellemalkin.comatrios.blogspot.comlittlegreenfootballs.comwashingtonmonthly.compowerlineblog.comdrudgereport.com
conservativeliberalconservativeconservativeliberal
We have at least 2 labels
per class. Stop.
Overview
• Preview• MultiRankWalk– Random Walk with Restart– RWR for Classification
• Seed Preference• Experiments• Results• The Question
Results
• MRW vs. wvRN with random seed preference
MRW drastically better with a small number of
seed labels; performance not significantly different with larger numbers of
seeds
MRW does extremely well with just one
randomly selected label
per class!
Averaged over 20 runs
Results
• wvRN with different seed preferences
LinkCount or PageRank
much better than Random with smaller number of seed labels
PageRank slightly better
than LinkCount, but in general not significantly so
Results
• Does MRW benefit from seed preference?
Yes, on certain datasets with small number of seed labels;
note the already very high F1 on
most datasets
A rare instance where
authoritative seeds hurt
performance, but not
statistically significant
Results• How much better is MRW using authoritative seed preference?
y-axis:MRW F1
score minus wvRN F1
x-axis: number of seed
labels per classThe gap between
MRW and wvRN narrows with authoritative
seeds, but they are still
prominent on some datasets
with small number of seed
labels
Results
• Summary– MRW much better than wvRN with small number
of seed labels– MRW more robust to varying quality of seed labels
than wvRN– Authoritative seed preference boosts algorithm
effectiveness with small number of seed labels
We recommend MRW and authoritative seed preference as a strong baseline for semi-supervised classification on network data
Overview
• Preview• MultiRankWalk– Random Walk with Restart– RWR for Classification
• Seed Preference• Experiments• Results• The Question
The Question• What really makes MRW and wvRN different?• Network-based SSL often boil down to label propagation. • MRW and wvRN represent two general propagation methods –
note that they are call by many names:MRW wvRN
Random walk with restart Reverse random walk
Regularized random walk Random walk with sink nodes
Personalized PageRank Hitting time
Local & global consistency Harmonic functions on graphs
Iterative averaging of neighbors
Great…but we still don’t know why the differences in
their behavior on these network datasets!
The Question• It’s difficult to answer exactly why MRW does better with a smaller
number of seeds.• But we can gather probable factors from their propagation models:
MRW wvRN
1 Centrality-sensitive Centrality-insensitive
2 Exponential drop-off / damping factor No drop-off / damping
3Propagation of different classes done independently
Propagation of different classes interact
The Question• An example from a political blog dataset – MRW vs. wvRN
scores for how much a blog is politically conservative:1.000 neoconservatives.blogspot.com1.000 strangedoctrines.typepad.com1.000 jmbzine.com0.593 presidentboxer.blogspot.com0.585 rooksrant.com0.568 purplestates.blogspot.com0.553 ikilledcheguevara.blogspot.com0.540 restoreamerica.blogspot.com0.539 billrice.org0.529 kalblog.com0.517 right-thinking.com0.517 tom-hanna.org0.514 crankylittleblog.blogspot.com0.510 hasidicgentile.org0.509 stealthebandwagon.blogspot.com0.509 carpetblogger.com0.497 politicalvicesquad.blogspot.com0.496 nerepublican.blogspot.com0.494 centinel.blogspot.com0.494 scrawlville.com0.493 allspinzone.blogspot.com0.492 littlegreenfootballs.com0.492 wehavesomeplanes.blogspot.com0.491 rittenhouse.blogspot.com0.490 secureliberty.org0.488 decision08.blogspot.com0.488 larsonreport.com
0.020 firstdownpolitics.com0.019 neoconservatives.blogspot.com0.017 jmbzine.com0.017 strangedoctrines.typepad.com0.013 millers_time.typepad.com0.011 decision08.blogspot.com0.010 gopandcollege.blogspot.com0.010 charlineandjamie.com0.008 marksteyn.com0.007 blackmanforbush.blogspot.com0.007 reggiescorner.blogspot.com0.007 fearfulsymmetry.blogspot.com0.006 quibbles-n-bits.com0.006 undercaffeinated.com0.005 samizdata.net0.005 pennywit.com0.005 pajamahadin.com0.005 mixtersmix.blogspot.com0.005 stillfighting.blogspot.com0.005 shakespearessister.blogspot.com0.005 jadbury.com0.005 thefulcrum.blogspot.com0.005 watchandwait.blogspot.com0.005 gindy.blogspot.com0.005 cecile.squarespace.com0.005 usliberals.about.com0.005 twentyfirstcenturyrepublican.blogspot.com
Seed labels underlined
1. Centrality-sensitive: seeds have different
scores and not necessarily the highest
2. Exponential drop-off: much less sure
about nodes further away from seeds
3. Classes propagate independently:
charlineandjamie.com is both very likely a conservative and a
liberal blog (good or bad?)
We still don’t completely understand it yet.
Questions?
Related Work
• MRW is very much related to– “Local and global consistency” (Zhou et al. 2004)– “Web content categorization using link information”
(Gyongyi et al. 2006)– “Graph-based semi-supervised learning as a generative
model” (He et al. 2007)• Seed preference is related to the field of active learning– Active learning chooses which data point to label next based
on previous labels; the labeling is interactive– Seed preference is a batch labeling method
Similar formulation,
different view
RWR ranking as features to SVM
Random walk
without restart,
heuristic stopping
Authoritative seed preference a good base line for active
learning on network data!
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