Modeling and Evaluating Trust Network Inference

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dc.contributor.authorDing, Li
dc.contributor.authorKolari, Pranam
dc.contributor.authorGanjugunte, Shashidhara
dc.contributor.authorFinin, Tim
dc.contributor.authorJoshi, Anupam
dc.date.accessioned2018-12-13T16:05:46Z
dc.date.available2018-12-13T16:05:46Z
dc.date.issued2004-07-19
dc.descriptionSeventh International Workshop on Trust in Agent Societies at AAMAS 2004en_US
dc.description.abstractThe growth in knowledge sharing enabled by the (Semantic) Web has made trust an increasingly critical issue. Based on explicit inter-agent trust relations, a trust network emerges on the (Semantic) Web in the knowledge sharing context. The concept of a trust network and its application to knowledge sharing have received recent attention but neither their structural properties (e.g. dynamics, complexity) nor inference mechanisms (e.g. trust discovery, trust evolution, trust propagation) have been well addressed. This paper formalizes trust network inference notions, providing both data and computational models, and suggests an evaluation model for benchmarking. The data model clari- fies the data (context, restriction, output) used by trust network inference for knowledge sharing. It also elaborates trust network representation and articulates different types of trust. The computational model reviews graph theory and referral network interpretations of trust network inference and proposes a new one that treats trust network as an emergent property. This new model supports both trust evolution and trust propagation. The evaluation model describes metrics as well as methods to generate test scenarios and data. We argue that this approach is more customizable, flexible and scalable than traditional approaches such as public reputation systems and collaborative filtering.en_US
dc.description.sponsorshipPartial research support was provided by DARPA contract F30602- 00-0591 and by NSF by awards NSF-ITR-IIS-0326460 and NSF-ITRIDM- 0219649.en_US
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/170/Modeling-and-Evaluating-Trust-Network-Inferenceen_US
dc.format.extent12 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M2R49GD5V
dc.identifier.urihttp://hdl.handle.net/11603/12252
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectmodeling
dc.subjecttrust network
dc.subjectUMBC Ebiquity Research Group
dc.subjectsemantic web
dc.subjectnetwork interpretation
dc.titleModeling and Evaluating Trust Network Inferenceen_US
dc.typeTexten_US

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