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Author Jiang, Wenjun ♦ Wang, Guojun ♦ Bhuiyan, Zakirul Alam ♦ Wu, Jie
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Trusted graph ♦ Analogy ♦ Online social networks (OSNs) ♦ Simplification ♦ Trust evaluation ♦ Trust models
Abstract Online Social Networks (OSNs) are becoming a popular method of meeting people and keeping in touch with friends. OSNs resort to trust evaluation models and algorithms to improve service quality and enhance user experiences. Much research has been done to evaluate trust and predict the trustworthiness of a target, usually from the view of a source. Graph-based approaches make up a major portion of the existing works, in which the trust value is calculated through a trusted graph (or trusted network, web of trust, or multiple trust chains). In this article, we focus on graph-based trust evaluation models in OSNs, particularly in the computer science literature. We first summarize the features of OSNs and the properties of trust. Then we comparatively review two categories of graph-simplification-based and graph-analogy-based approaches and discuss their individual problems and challenges. We also analyze the common challenges of all graph-based models. To provide an integrated view of trust evaluation, we conduct a brief review of its pre- and postprocesses (i.e., the preparation and validation of trust models, including information collection, performance evaluation, and related applications). Finally, we identify some open challenges that all trust models are facing.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-05-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 49
Issue Number 1
Page Count 35
Starting Page 1
Ending Page 35

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Source: ACM Digital Library