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

2. Trust A statement or prediction of reliance A statement or prediction of reliance Examples 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 The Security Approach –Authentication, Access Control, Digital Signatures, Public Keys, etc Insitutional Approach / Central Authority Insitutional Approach / Central Authority Trust Networks (Social Approach): Direct experiences and reputation 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 discovery Trust value Trust value Trust propagation Trust propagation Trust aggregation Trust aggregation Trust update / learning --- regret, forgiveness, Trust update / learning --- regret, forgiveness,

7. Social Networks: Graph Models Small world networks 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 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”  = 0  = 0.125  = 1 People know others at random. Not clustered, but “small world” People know their neighbors, and a few distant people. Clustered and “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! 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. b model reproduces short-path results of random graphs, but also allow for clustering. Small-world phenomena occur at threshold between order and chaos. 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. 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 ABCAB * From [2]

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

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

13. Inferring Trust [3] Three operators: 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]

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

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

18. Other approaches Game theoretic Game theoretic Emergence Interpretation of Trust 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 1. Guillaume Muller, Laurent Vercouter. 2008. Computational Trust and Reputation Models, AAMAS’08 Tutorial 2. 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. 3. Hang, C., Wang, Y., and Singh, M. P. 2009. Operators for propagating trust and their evaluation in social networks. In Proceedings of AAMAS09 4. Li Ding, Pranam Kolari, Shashidhara Ganjugunte, Tim Finin, Anupam Joshi. 2004. Modeling and evaluating trust network inference. In Proceedings of the 7th International Workshop on Trust in Agent Societies