Show simple item record

dc.contributor.authorŞensoy, Murat
dc.contributor.authorYilmaz, B.
dc.contributor.authorNorman, T. J.
dc.date.accessioned2016-02-15T07:33:14Z
dc.date.available2016-02-15T07:33:14Z
dc.date.issued2016-02
dc.identifier.isbn1467-8640
dc.identifier.urihttp://hdl.handle.net/10679/2148
dc.identifier.urihttp://onlinelibrary.wiley.com/doi/10.1111/coin.12046/abstract
dc.description.abstractBootstrapping trust assessment where there is little or no evidence regarding a subject is a significant challenge for existing trust and reputation systems. When direct or indirect evidence is absent, existing approaches usually assume that all agents are equally trustworthy. This naive assumption makes existing approaches vulnerable to attacks such as Sybil and whitewashing. Inspired by real-life scenarios, we argue that malicious agents may share some common patterns or complex features in their descriptions. If such patterns or features can be detected, they can be exploited to bootstrap trust assessments. Based on this idea, we propose the use of frequent subgraph mining and state-of-the-art knowledge representation formalisms to estimate a priori trust for agents. Our approach first discovers significant patterns that may be used to characterize trustworthy and untrustworthy agents. Then, these patterns are used as features to train a regression model to estimate the trustworthiness of agents. Last, a priori trust for unknown agents (e.g., newcomers) is estimated using the discovered features based on the trained model. Through empirical evaluation, we show that the proposed approach significantly outperforms well-known trust approaches if trustworthiness of agents is correlated with patterns in their descriptions or social networks. Furthermore, we show that the proposed approach performs at least as good as the existing approaches if such correlations do not exist.
dc.description.sponsorshipU.S. Army Research Laboratory ; U.K. Ministry of Defence ; U.S. Army Research Laboratory ; TÜBİTAK
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relationinfo:turkey/grantAgreement/TUBITAK/113E238
dc.relation.ispartofComputational Intelligence
dc.rightsrestrictedAccess
dc.titleStage: stereotypical trust assessment through graph extractionen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.contributor.departmentŎzyeğin University
dc.contributor.authorID(ORCID 0000-0001-8806-4508 & YÖK ID 41438) Şensoy, Murat
dc.contributor.ozuauthorŞensoy, Murat
dc.identifier.volume32
dc.identifier.issue1
dc.identifier.startpage72
dc.identifier.endpage101
dc.identifier.wosWOS:000369837800003
dc.identifier.doi10.1111/coin.12046
dc.subject.keywordsTrust and reputationen_US
dc.subject.keywordsSemantic Weben_US
dc.subject.keywordsGraph miningen_US
dc.identifier.scopusSCOPUS:2-s2.0-84957426398
dc.contributor.authorMale1


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record


Share this page