research-article

Heterogeneous network-based trust analysis: a survey

Authors:

Jiawei HanAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 13, Issue 1

Pages 54 - 71

https://doi.org/10.1145/2031331.2031341

Published: 31 August 2011 Publication History

Abstract

Different information sources publish information with different degrees of correctness and originality. False information can often result in considerable damage. Hence, trustworthinessof information is an important issue in this datadriven world economy. Reputation of different agents in a network has been studied earlier in a variety of domains like e-commerce, social sciences, sensor networks, and P2P networks. Recently there has been work in the data mining community on performing trust analysis based on the data provided by multiple information providers for different objects, and such agents and their provided information about data objects form a multi-typed heterogeneous network. The trust analysis under such a framework is considered as heterogeneous network-based trust analysis. This paper will survey heterogeneous network-based trust analysis models and their applications. We would conclude with a summary and some thoughts on future research in the area.

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Heterogeneous network-based trust analysis: a survey

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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 13, Issue 1

June 2011

79 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/2031331

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Copyright © 2011 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 August 2011

Published in SIGKDD Volume 13, Issue 1

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Cited By

Potyka NBooth RMarquis POrtiz MPagnucco M(2024)Balancing open-mindedness and conservativeness in quantitative bipolar argumentation (and how to prove semantical from functional properties)Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning10.24963/kr.2024/56(597-607)Online publication date: 2-Nov-2024
https://dl.acm.org/doi/10.24963/kr.2024/56
Sarıkoz S(2023)Examining Knowledge Extraction Processes from Heterogeneous Data SourcesBrilliant Engineering10.36937/ben.2023.47984:1(1-8)Online publication date: 8-Feb-2023
https://doi.org/10.36937/ben.2023.4798
Singleton JBooth R(2022)Towards an axiomatic approach to truth discoveryAutonomous Agents and Multi-Agent Systems10.1007/s10458-022-09569-336:2Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10458-022-09569-3
Englmeier K(2021)The Role of Text Mining in Mitigating the Threats from Fake News and Misinformation in Times of CoronaProcedia Computer Science10.1016/j.procs.2021.01.115181(149-156)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.01.115
Singleton JBooth REl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)An Axiomatic Approach to Truth DiscoveryProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3399058(2011-2013)Online publication date: 5-May-2020
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Li YWang YBi CJiang X(2018)Revisiting transductive support vector machines with margin distribution embeddingKnowledge-Based Systems10.1016/j.knosys.2018.04.017152:C(200-214)Online publication date: 15-Jul-2018
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Prandi CMirri SFerretti SSalomoni P(2017)On the Need of Trustworthy Sensing and Crowdsourcing for Urban Accessibility in Smart CityACM Transactions on Internet Technology10.1145/313332718:1(1-21)Online publication date: 26-Oct-2017
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Marshall JArgueta AWang D(2017)A Neural Network Approach for Truth Discovery in Social Sensing2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)10.1109/MASS.2017.26(343-347)Online publication date: Oct-2017
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