Download Computing Trust in Social Networks

Computing Trust in Social
Networks
Huy Nguyen
Lab seminar April 15, 2011
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Web-Based Social Networks
(WBSNs)
• Websites and interfaces that let people
maintain browsable lists of friends
• Last count (2008)
– 245 social networking websites
– Over 850,000,000 accounts
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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
3
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
4
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
6
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.
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Source
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Sink
TidalTrust 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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SUNNY
• Trust inference algorithm using Bayesian
Networks
• Trust network is mapped into a Bayes Net
• Conditional probability values are computed
through profile similarity measures
• A “most confident” subnetwork is selected and
trust inference is performed on that network
• Result is an inferred trust value and a confidence
in that value
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Confidence in Social Networks
• P(n|n’): prob that n believes n’
• Calculate P(n|n’) based on profile
similarity
1. Overall difference Ө
2. Difference on extreme χ
3. Maximum difference ∆
4. Correlation coefficient σ
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Compute confidence
σ |1 – 2(0.7 Ө + 0.2 ∆ + 0.1 χ ) |
if χ exists
P(n|n’) =
σ |1 – 2(0.8 Ө + 0.2 ∆ ) |
otherwise
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Bayesian Network of Trust
• Recursively do
– Backward breath-first search from the
source
– Forward breath-first search from the sink
• Final result: set K
• Return FAILURE if the source node is
not in K
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Source
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Sink
SUNNY algorithm
1. Build a Bayes Net of the trust domain
2. Compute conditional prob of each
node in BN
3. Use the conditional prob to decide if
the node is trusted or not
4. Use TidalTrust to compute the trust
value
15
Evaluation: FilmTrust
• Movie recommender
• Website has social network where users
rate how much they trust their friends
about movies
• Movie recommendations are made
using trust
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Evaluation
• Movie rating is used to compute
confidence values
• SUNNY vs. TidalTrust on FilmTrust
network
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Conclusions
• Trust is an important relationship in social
networks
• Introduced a probabilistic interpretation of
confidence in trust network
• Proposed SUNNY an algorithm for computing
trust and confidence in social networks
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