1. 1 Computing Trust in Social Networks Huy Nguyen Lab seminar April 15, 2011

2. 2 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

3. 3 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

4. 4 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

5. 5 Research Areas Inferring Trust Relationships Using Trust in Applications

6. 6 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. ABC t AB t BC t AC

7. 7 Methods TidalTrust –Personalized trust inference algorithm SUNNY –Bayes Network algorithm that computes trust inferences and a confidence interval on the inferred value.

8. 8 Source Sink

9. 9 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

10. 10 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

11. 11 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

12. 12 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

13. 13 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

14. 14 Source Sink

15. 15 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

16. 16 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

17. 17 Evaluation Movie rating is used to compute confidence values SUNNY vs. TidalTrust on FilmTrust network

18. 18 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