The introduction of trust-based approaches in social scenarios modeled as multi-agent systems (MAS) has been recognized as a valid solution to improve the effectiveness of these communities. In fact, they make interactions taking place in social scenarios much fruitful as possible, limiting or even avoiding malicious or fraudulent behaviors, including collusion. This is also the case of multi-layered neural networks (NN), which can face limited, incomplete, misleading, controversial or noisy datasets, produced by untrustworthy agents. Many strategies to deal with malicious agents in social networks have been proposed in the literature. One of the most effective is represented by Eigentrust, often adopted as a benchmark. It can be seen as a variation of PageRank, an algorithm for determining result rankings used by search engines like Google. Moreover, Eigentrust can also be viewed as a linear neural network whose architecture is represented by the graph of Web pages. A major drawback of Eigentrust is that it uses some additional information about agents that can be a priori considered particularly trustworthy, rewarding them in terms of reputation, while the non pre-trusted agents are penalized. In this paper, we propose a different strategy to detect malicious agents which does not modify the real reputation values of the honest ones. We introduce a measure of effectiveness when computing reputation in presence of malicious agents. Moreover, we define a metric of error useful to quantitatively determine how much an algorithm for the identification of malicious agents modifies the reputation scores of the honest ones. We have performed an experimental campaign of mathematical simulations on a dynamic multi-agent environment. The obtained results show that our method is more effective than Eigentrust in determining reputation values, presenting an error which is about a thousand times lower than the error produced by Eigentrust on medium-sized social networks.

中文翻译：

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提高 Eigentrust 在存在共谋的情况下计算社会代理声誉的有效性。

在建模为多代理系统 (MAS) 的社交场景中引入基于信任的方法已被认为是提高这些社区有效性的有效解决方案。事实上，它们使社交场景中发生的互动尽可能富有成效，限制甚至避免恶意或欺诈行为，包括共谋。多层神经网络 (NN) 也是如此，它可能面临由不可信代理生成的有限、不完整、误导性、有争议或嘈杂的数据集。文献中已经提出了许多处理社交网络中恶意代理的策略。最有效的方法之一是以 Eigentrust 为代表，通常被用作基准。它可以被视为 PageRank 的变体，PageRank 是一种用于确定 Google 等搜索引擎使用的结果排名的算法。此外，Eigentrust 还可以被视为一个线性神经网络，其架构由网页图表示。Eigentrust 的一个主要缺点是，它使用了一些有关代理的附加信息，这些信息可以先验地被认为是特别值得信赖的，从而在声誉方面奖励他们，而不预先信任的代理则受到惩罚。在本文中，我们提出了一种不同的策略来检测恶意代理，该策略不会修改诚实代理的真实声誉值。我们引入了在存在恶意代理的情况下计算信誉时的有效性度量。此外，我们定义了一个错误度量，可用于定量确定用于识别恶意代理的算法对诚实代理的声誉评分的修改程度。我们在动态多智能体环境中进行了数学模拟的实验活动。获得的结果表明，我们的方法在确定声誉值方面比 Eigentrust 更有效，其误差比 Eigentrust 在中型社交网络上产生的误差低约一千倍。