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Understanding Graph-Based Trust Evaluation in Online Social Networks: Methodologies and Challenges

Authors:

Md Zakirul Alam Bhuiyan,

Jie WuAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 49, Issue 1

Article No.: 10, Pages 1 - 35

https://doi.org/10.1145/2906151

Published: 23 May 2016 Publication History

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.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 49, Issue 1

March 2017

705 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2911992

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering/University of Florida/Gainesville, FL

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Publication History

Published: 23 May 2016

Accepted: 01 February 2016

Revised: 01 November 2015

Received: 01 December 2014

Published in CSUR Volume 49, Issue 1

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https://doi.org/10.1109/TSMC.2024.3452931
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