Computing Trust in Social
Networks
Jennifer Golbeck
College of Information Studies
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Web-Based Social Networks
(WBSNs)
• Websites and interfaces that let people
maintain browsable lists of friends
• Last count
– 245 social networking websites
– Over 850,000,000 accounts
– Full list at http://trust.mindswap.org
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Using WBSNs
• Lots of users, spending lots of time
creating public information about their
preferences
• We should be able to use that to build
better applications
• When I want a recommendation, who
do I ask?
– The people I trust
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Applications of Trust
• With direct knowledge or a
recommendation about how much to
trust people, this value can be used as
a filter in many applications
• Since social networks are so prominent
on the web, it is a public, accessible
data source for determining the quality
of annotations and information
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Research Areas
• Inferring Trust Relationships
• Using Trust in Applications
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Inferring Trust
The Goal: Select two individuals - the
source (node A) and sink (node C) - and
recommend to the source how much to
trust the sink.
t
AC
A
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tAB
B
tBC
C
Methods
• TidalTrust
– Personalized trust inference algorithm
• SUNNY
– Bayes Network algorithm that computes
trust inferences and a confidence interval
on the inferred value.
• Profile Based
– Trust from similarity
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Source
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Sink
Trust Algorithm
• If the source does not know the sink, the
source asks all of its friends how much to
trust the sink, and computes a trust value by
a weighted average
• Neighbors repeat the process if they do not
have a direct rating for the sink
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Accuracy
• Comparison to other algorithms
– Beth-Borcherding-Klein (BBK) 1994
Networ k
Trust Project
FilmTrust
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Algorithm
TidalTrust
BBK
1.09 (.99)
1.59 (1.42)
1.35 (1.23)
2.75 (1.80)
Trust from Similarity
• We know trust correlates with overall similarity
(Ziegler and Golbeck, 2006)
• Does trust capture more than just overall agreement?
• Two Part Analysis
– Controlled study to find profile similarity measures that relate
to trust
– Verification through application in a live system
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Experimental Outline
• Phase 1: Rate Movies - Subjects rate movies
on the list
– Ratings grouped as extreme (1,2,9,10) or far from
average (≥4 different)
• Create profiles of hypothetical users
– Profile is a list of movies and the hypothetical
user’s ratings of them
• Subjects rate how much they would trust the
person represented by the profile
– Vary the profile’s ratings in a controlled way
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Generating Profiles
• Each profile contained exactly 10 movies, 4
from an experimental category and 6 from its
complement
– E.g. 4 movies with extreme ratings and 6 with nonextreme ratings
• Control for average difference, standard
deviation, etc. so we could see how
differences on specific categories of films
affected trust
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Example Profile
• Movies m1 through m10
• User ratings r1…r10 for
m1…m10
– r1…r4 are extreme
(1,2,9, or 10)
– r5…r10 are not extreme
• Profile ratings pi = ri§i
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Results
1. Reconfirmed that trust strongly
correlates with overall similarity ().
2. Agreement on extremes ()
3. Largest single difference (r)
4. Subject’s propensity to trust ()
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Extreme Ratings
• When high are used on movies with extreme
ratings, the trust ratings are significantly lower
than when low are applied to those films
• Statistically significant for all i
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Maximum Difference (r)
• Holding overall agreement and standard
deviation constant, trust decreased as the
single largest difference between the profile
and the subject (r) increased.
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Propensity to Trust ()
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Validation
• Gather all pairs of FilmTrust users who
have a known trust relationship and
share movies in common
– 322 total user pairs
• Develop a formula using the
experimental parameters to estimate
trust
• Compute accuracy by comparing
computed trust value with known value
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In FilmTrust
Use weights (w1,w2, w3, w4, w) = (7,2,1,8,2)
Correlation
Absolute Mean Error
Std. Dev of Mean Error
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Overall
Similarity
Only
0.24
1.91
1.95
Our
Formula
0.73
1.13
0.95
Effect of change
• If a node changes it’s trust value for
another, that will propagate through the
inferred values
• How far? What is the magnitude? Does
the impact increase or decrease with
distance?
• How does this relate to the algorithm?
• Joint work with Ugur Kuter
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Algorithms Considered
• Eigentrust
– Global algorithm
– Like PageRank, but with weighted edges
• Advogato
– Finds paths through the network
– Global group trust metric that uses a set of
authoritative nodes to decide how trustworthy a
person is
• TidalTrust
• TidalTrust++
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– No minimum distance - search the entire network
Initial ideas?
• The further you get from the sink, the
smaller the impact.
• Changes by more central, highly
connected nodes will create a bigger
impact.
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Network
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Methodology
• Pick a pair of nodes in the network
–
–
–
–
–
Set trust to 0
Infer trust values between all pairs
Set trust to 1
Infer trust value between all pairs
Compare inferred values from trust=0 to trust=1
• Repeat for every pair
• Repeat for each algorithm
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Fraction of Nodes at a Given Distance Whose
Inferred Trust Value for the Sink Changed
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Source
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Sink
Average Magnitude of Change
at a Given Distance
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Conclusions and
Future Directions
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Conclusions
• Trust is an important relationship in social
networks.
• Social relationships are different than other
common data used in CS research.
• Trust can be computed in a variety of ways
• The type of algorithm and behavior of users
in the network impact the stability of trust
inferences
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Future Work - Computing with Trust
• Major categories of trust inference:
global vs. local, same scale vs. new
scale
– All have algorithms
• Additional features (like confidence)
• Hybrid approaches
– Use trust assigned by users and similarity
– Use multiple relationships for better
certainty in certain domains (e.g. authority)
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Future Work - Applications
• What sort of applications can trust be
used to support?
• Recommender systems, email filtering,
tagging, information ranking
• Disaster response
– Highlight relevant items among vast
collections of data
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• Jennifer Golbeck
• golbeck@cs.umd.edu
• http://www.cs.umd.edu/~golbeck
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