Computing Trust in Social Networks

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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.
tAC
A
B
C
tAB
tBC
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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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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

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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
  users ratings of them
• Subjects rate how much they would trust the
  person represented by the profile
• Vary the profiles 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
  non-extreme 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 r1r10 for m1m10
• r1r4 are extreme (1,2,9, or 10)
• r5r10 are not extreme
• Profile ratings pi ri?i

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Results

• Reconfirmed that trust strongly correlates with
  overall similarity (?).
• Agreement on extremes (??)
• Largest single difference (r)
• Subjects 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)

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Effect of change

• If a node changes its 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
• No minimum distance - search the entire network

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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 trust0 to trust1
• 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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Average Magnitude of Change at a Given Distance
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Conclusions andFuture 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_at_cs.umd.edu
• http//www.cs.umd.edu/golbeck