The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Communications of the ACM - Medical image modeling
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Trust network analysis with subjective logic
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
Inferring binary trust relationships in Web-based social networks
ACM Transactions on Internet Technology (TOIT)
SUNNY: a new algorithm for trust inference in social networks using probabilistic confidence models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Statistical relational learning of trust
Machine Learning
Content-driven trust propagation framework
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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In social networks, notions such as trust, fondness, or respect between users can be expressed by associating a strength with each tie. This provides a view of social interaction as a weighted graph. Sociological models for such weighted networks can differ significantly in their basic motivations and intuitions. In this paper, we present a flexible framework for probabilistic modeling of social networks that allows one to represent these different models and more. The framework, probabilistic soft logic (PSL), is particularly well-suited for this domain, as it combines a declarative, first-order logic-based syntax for describing relational models with a soft-logic representation, which maps naturally to the non-discrete strength of social trust. We demonstrate the flexibility and effectiveness of PSL for trust prediction using two different approaches: a structural balance model based on social triangles, and a social status model based on a consistent status hierarchy. We test these models on real social network data and find that PSL is an effective tool for trust prediction.