基于博弈论的社交网络信任测量模型。

Q1 Mathematics
Computational Social Networks Pub Date : 2016-01-01 Epub Date: 2016-05-20 DOI:10.1186/s40649-016-0027-x
Yingjie Wang, Zhipeng Cai, Guisheng Yin, Yang Gao, Xiangrong Tong, Qilong Han
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引用次数: 28

摘要

背景:在社会网络中,信任是一个复杂的社会网络。在线社交网络的参与者希望与尽可能多的可靠用户分享信息和体验。然而,信任的建模是复杂的,并且依赖于应用程序。建立信任模型需要考虑交互历史、推荐、用户行为等。因此,建立信任模型是在线社交网络的一个重要关注点。方法:提出基于博弈论的社交网络信任测量模型。信誉度从服务可靠性、反馈有效性、推荐可信度三个方面进行计算,得到更准确的结果。此外,为了缓解搭便车问题,我们分别针对特定信任和全局信任提出了基于博弈论的惩罚机制。结果与结论:我们证明了所提出的信任度量模型是有效的。通过增加所提出的惩罚机制,可以有效地解决搭便车问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A game theory-based trust measurement model for social networks.

A game theory-based trust measurement model for social networks.

A game theory-based trust measurement model for social networks.

A game theory-based trust measurement model for social networks.

Background: In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus for online social networks.

Methods: We propose a game theory-based trust measurement model for social networks. The trust degree is calculated from three aspects, service reliability, feedback effectiveness, recommendation credibility, to get more accurate result. In addition, to alleviate the free-riding problem, we propose a game theory-based punishment mechanism for specific trust and global trust, respectively.

Results and conclusions: We prove that the proposed trust measurement model is effective. The free-riding problem can be resolved effectively through adding the proposed punishment mechanism.

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来源期刊
Computational Social Networks
Computational Social Networks Mathematics-Modeling and Simulation
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.
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