Abstract
Computing trust between individuals in social networks is important for many intelligent systems that take advantage of reasoning in social situations. There have been many algorithms developed for inferring trust relationships in a variety of ways. These algorithms all work on a snapshot of the network; that is, they do not take into account changes in trust values over time. However, trust between people is always changing in realistic social networks and when changes happen, inferred trust values in the network will also change. Under these circumstances, the behavior the existing trust-inference algorithms is not yet very well understood. In this paper, we present an experimental study of several types of trust inference algorithms to answer the following questions on trust and change:
• How far does a single change propagate through the network?
• How large is the impact of that change?
• How does this relate to the type of inference algorithm?
Our experimental results provide insights into which algorithms are most suitable for certain applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Prasanna Kumar Desikan, Nishith Pathak, Jaideep Srivastava, and Vipin Kumar. Invremental page rank computation on evolving graphs. In WWW (Special interest tracks and posters), pages 1094–1095, 2005.
Jennifer Golbeck. Computing and Applying Trust in Web-based Social Networks. PhD thesis, University of Maryland, College Park, MD, April 2005.
Jennifer Golbeck. Generating predictive movie recommendations from trust in social networks. In Proceedings of the Fourth International Conference on Trust Management, 2006.
Sepandar D. Kamvar, Mario T. Schlosser, and Hector Garcia-Molina. The eigentrust algorithm for reputation management in p2p networks. In WWW ’03: Proceedings of the 12th international conference on World Wide Web, pages 640–651, ACM Press, New York, NY, USA, 2003.
Ugur Kuter and Jennifer Golbeck: Sunny: A new algorithm for trust inference in social networks, using probabilistic confidence models. In Proceedings of the National Conference on Artifical Intelligence (AAAI), 2007.
A. Langville and C. Meyer. Updating pagerank with iterative aggregation. In Proceedings of the 13th World Wide Web Conference, 2004.
Amy Nicole Langville and carl Dean Meyer. Survey: Deeper inside pagerank. Internet Mathematics, 1(3), 2003.
Raph Levien and Alex Aiken. Attack-resistant trust metrics for public key certification. In 7th USENIX Security Symposium, pages 229–242, 1998.
P. Massa and P. Avesani. Trust-aware collaborative filtering for recommender systems. In Proc. of Federated int. Conference On The Move to Meaningful Internet: CoopsIS, DOA, ODBASE, 2004.
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998.
Matthew Richardson, R. Agrawal, and P. Domingos. Trust management for the semantic web. In Proceedings of the Second International Semantic Web Conference, 2003.
Cai-Nicolas Ziegler and Georg Lausen. Spreading activation models for trust propagation. In Proceedings of the IEEE International Conference on e-Technology, e-Commerce, and e-service. IEEE Computer Society Press, Taipei, Taiwan, March 2004.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag London Limited
About this chapter
Cite this chapter
Golbeck, J., Kuter, U. (2009). The Ripple Effect: Change in Trust and Its Impact Over a Social Network. In: Golbeck, J. (eds) Computing with Social Trust. Human–Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-84800-356-9_7
Download citation
DOI: https://doi.org/10.1007/978-1-84800-356-9_7
Publisher Name: Springer, London
Print ISBN: 978-1-84800-355-2
Online ISBN: 978-1-84800-356-9
eBook Packages: Computer ScienceComputer Science (R0)