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首页> 外文期刊>Knowledge-Based Systems >Characterizing and using gullibility, competence, and reciprocity in a very fast and robust trust and distrust inference algorithm for weighted signed social networks
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Characterizing and using gullibility, competence, and reciprocity in a very fast and robust trust and distrust inference algorithm for weighted signed social networks

机译：在加权签名社交网络的一种非常快速，强大的信任和不信任推理算法中表征和使用可信性，能力和对等

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摘要

Predicting trust is a classic problem in social networks analysis. Furthermore, while most early approaches ignore distrust, recent works seem to consider it as important, if not more important, than trust itself. In this paper, we present a novel approach to predict both trust and distrust in Weighted Signed Social Networks very efficiently and in a satisfyingly accurate and robust way. Therefore allowing people to have healthier online presence and interactions.Being a local metric that does not rely on trust propagation, the proposed approach does not suffer from some serious limitations like trust decay, opinions conflict, path dependence, and time complexity. Moreover, our experiments on four real-world datasets show that, in addition to its simplicity and extensibility, this algorithm is robust to network sparsity, and provides satisfyingly accurate and very fast predictions. (C) 2019 Elsevier B.V. All rights reserved.
机译：预测信任是社交网络分析中的经典问题。此外，尽管大多数早期方法都忽略了不信任，但最近的工作似乎认为它比信任本身更重要，甚至更重要。在本文中，我们提出了一种新颖的方法来非常有效地并且以令人满意的准确和健壮的方式来预测加权签名社交网络中的信任和不信任。因此，使人们可以拥有更健康的在线状态和交互。作为一种不依赖信任传播的本地度量标准，该方法不受诸如信任衰减，观点冲突，路径依赖和时间复杂性等严重限制的困扰。此外，我们在四个真实世界数据集上的实验表明，该算法除了具有简单性和可扩展性之外，还对网络稀疏性具有鲁棒性，并且可以提供令人满意的准确且非常快速的预测。 （C）2019 Elsevier B.V.保留所有权利。

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