Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation

Evaluating trust and distrust between users in online social networks is an important research problem. To address this problem, we provide a method for estimating continuous trust /distrust value between unconnected users. Our method is based on co-citation and transpose trust propagation. We deter...

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Main Authors: Safi Ullah Nasir, Tae-Hyung Kim
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9007462/
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spelling doaj-7211c248171f4b718742828f049485322021-03-30T02:09:08ZengIEEEIEEE Access2169-35362020-01-018413624137110.1109/ACCESS.2020.29757829007462Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust PropagationSafi Ullah Nasir0Tae-Hyung Kim1https://orcid.org/0000-0002-3001-1080Department of Computer Science and Engineering, Hanyang University at ERICA, Ansan, South KoreaDepartment of Computer Science and Engineering, Hanyang University at ERICA, Ansan, South KoreaEvaluating trust and distrust between users in online social networks is an important research problem. To address this problem, we provide a method for estimating continuous trust /distrust value between unconnected users. Our method is based on co-citation and transpose trust propagation. We determine on average how differently two users trust or are trusted by other users, and how differently a user trusts another user from how it is trusted by that user. Using these differences, we estimate four partial trust estimates and compute the final trust value from trustor to trustee as the weighted average of these partial estimates. We propose a basic framework that maximizes accuracy, robustness and coverage and show how we can further improve the accuracy at a lower coverage. We perform experiments on real world trust related networks that show that our proposed method outperforms recent state of the art trust computation methods in terms of accuracy and robustness on commonly used datasets.https://ieeexplore.ieee.org/document/9007462/Trust computationdistrustco-citationreciprocityweighted signed networks
collection DOAJ
language English
format Article
sources DOAJ
author Safi Ullah Nasir
Tae-Hyung Kim
spellingShingle Safi Ullah Nasir
Tae-Hyung Kim
Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation
IEEE Access
Trust computation
distrust
co-citation
reciprocity
weighted signed networks
author_facet Safi Ullah Nasir
Tae-Hyung Kim
author_sort Safi Ullah Nasir
title Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation
title_short Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation
title_full Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation
title_fullStr Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation
title_full_unstemmed Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation
title_sort trust computation in online social networks using co-citation and transpose trust propagation
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Evaluating trust and distrust between users in online social networks is an important research problem. To address this problem, we provide a method for estimating continuous trust /distrust value between unconnected users. Our method is based on co-citation and transpose trust propagation. We determine on average how differently two users trust or are trusted by other users, and how differently a user trusts another user from how it is trusted by that user. Using these differences, we estimate four partial trust estimates and compute the final trust value from trustor to trustee as the weighted average of these partial estimates. We propose a basic framework that maximizes accuracy, robustness and coverage and show how we can further improve the accuracy at a lower coverage. We perform experiments on real world trust related networks that show that our proposed method outperforms recent state of the art trust computation methods in terms of accuracy and robustness on commonly used datasets.
topic Trust computation
distrust
co-citation
reciprocity
weighted signed networks
url https://ieeexplore.ieee.org/document/9007462/
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AT taehyungkim trustcomputationinonlinesocialnetworksusingcocitationandtransposetrustpropagation
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