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CLC number: TP391

On-line Access: 2017-12-04

Received: 2016-06-17

Revision Accepted: 2017-04-18

Crosschecked: 2017-11-01

Cited: 0

Clicked: 5436

Citations:  Bibtex RefMan EndNote GB/T7714


Xin Wang


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.10 P.1591-1600


Building trust networks in the absence of trust relations

Author(s):  Xin Wang, Ying Wang, Jian-hua Guo

Affiliation(s):  College of Computer Science and Technology, Jilin University, Changchun 130012, China; more

Corresponding email(s):   xinwangjlu@gmail.com, wangying2010@jlu.edu.cn

Key Words:  Trust network, Sparse learning, Homophily effect, Interaction behaviors

Xin Wang, Ying Wang, Jian-hua Guo. Building trust networks in the absence of trust relations[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1591-1600.

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author="Xin Wang, Ying Wang, Jian-hua Guo",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%A Xin Wang
%A Ying Wang
%A Jian-hua Guo
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%DOI 10.1631/FITEE.1601341

T1 - Building trust networks in the absence of trust relations
A1 - Xin Wang
A1 - Ying Wang
A1 - Jian-hua Guo
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 10
SP - 1591
EP - 1600
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601341

User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platforms. These issues pose a great challenge for predicting trust relations and further building trust networks. In this study, we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework, bTrust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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