Full Text:   <758>

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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: 2483

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xin Wang

http://orcid.org/0000-0002-3288-5195

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

http://doi.org/10.1631/FITEE.1601341


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.

@article{title="Building trust networks in the absence of trust relations",
author="Xin Wang, Ying Wang, Jian-hua Guo",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="10",
pages="1591-1600",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601341"
}

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%A Xin Wang
%A Ying Wang
%A Jian-hua Guo
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601341

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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
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SP - 1591
EP - 1600
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601341


Abstract: 
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.

不使用任何信任关系构建信任网络

概要:由于用户信任关系具有一定稀疏性和动态性,准确预测在线社交媒体中的信任关系变得较为困难;此外,大多数社交媒体平台都没有提供明确的信任关系。这些因素使得预测信任关系并构建信任网络具有一定挑战性。首先,验证了利用稀疏学习模型能够较好实现信任关系预测;然后,提出一个新颖框架bTrust,不使用任何信任关系,仅仅利用交互数据和同质性效应构建信任网络;最后,在Epinions和Ciao两个真实数据集上验证了bTrust框架的有效性,表明交互行为和同质性效应在构建信任网络中的重要性。

关键词:信任网络;稀疏学习;同质效应;交互行为

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

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