Trust and Reputation in Social Networks

1. Trust and Reputation in Social Networks Laura Zavala 03/2010

2. Trust • A statement or prediction of reliance • Examples • I believe that my doctor is a good surgeon • how much credence should I give to what this person says about agiven topic? • based on what my friends say, how much should I trust this newperson • CS Department at UMBC has a good reputation

3. Computing with Trust • The Security Approach • Authentication, Access Control, Digital Signatures, Public Keys, etc • Insitutional Approach / Central Authority • Trust Networks (Social Approach): Direct experiences and reputation • Collaborative Filtering (similar agents have similar beliefs) • Graph theory (trust propagation and inference) • Referral Networks (find chains of experts on a given topic)

4. Computing with Trust: Examples

5. Social Trust

6. Trust Models Issues • Trust discovery • Trust value • Trust propagation • Trust aggregation • Trust update / learning --- regret, forgiveness,

7. Social Networks: Graph Models • Small world networks • The small world concept suggests that any pair of entities in a seemingly vast, random network can actually connect relatively short paths of mutual acquaintances. • Properties of graph structures that define a small world network • clustering coefficient • average path length.

8. Graph Models: The Beta Model Watts and Strogatz (1998) “Link Rewiring” b = 0 b = 1 b = 0.125 People know their neighbors, and a few distant people. Clustered and “small world” People know others at random. Not clustered, but “small world” People know their neighbors. Clustered, but not a “small world”

9. Graph Models: The Beta Model Watts and Strogatz (1998) “Link Rewiring” • First five random links reduce the average path length of the network by half, regardless of N! • b model reproduces short-path results of random graphs, but also allow for clustering. • Small-world phenomena occur at threshold between order and chaos.

10. Inferring Trust [2] • 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 A A B B B C C tAB tBC * From [2]

11. Inferring Trust [2] • Binary values: 0 (no trust), 1 (trust) * From [2]

12. Inferring Trust [3] • Three operators: • Aggregation • Concatenation • Selection

13. Inferring Trust [3] • <b,d,u> • Three operators: • Aggregation • deals with the propagation of trust ratings along a path • Concatenation • chooses the most trust-worthy path to each witness • Selection • deals with the combination of trust ratings from paths between the same source and target

14. Inferring Trust [3]

15. Inferring Trust [3]

16. FilmTrust • http://trust.mindswap.org/FilmTrust • Combines online social network (w/trust) with movie ratings and reviews • Use trust inferences • To customize ratings • To sort reviews

17. FilmTrust • Combines trust, social networks, and movie ratings. • Preliminary results show that, in certain cases, the trust-based predictions outperform most other systems.

18. Other approaches • Game theoretic • Emergence Interpretation of Trust • “Our emergence interpretation enables agents to both discover and evolve trust knowledge for trust based operations”. Tim Finin, Anupam Joshi

19. References • Guillaume Muller, Laurent Vercouter. 2008. Computational Trust and Reputation Models, AAMAS’08 Tutorial • Jennifer Golbeck, James Hendler. 2006. FilmTrust: Movie recommendations using trust in web-based social networks. Proceedings of the IEEE Consumer Communications and Networking Conference , January 2006. • Hang, C., Wang, Y., and Singh, M. P. 2009. Operators for propagating trust and their evaluation in social networks. InProceedings ofAAMAS09 • Li Ding , Pranam Kolari , Shashidhara Ganjugunte , Tim Finin , Anupam Joshi. 2004. Modeling and evaluating trust network inference. InProceedings of the 7th International Workshop on Trust in Agent Societies