Trust network analysis has been widely applied in various fields, such as group recommendation, group decision-making and other related areas. In this paper, we focus on obtaining the complete trust network in which experts express their trust relationships for another with a single linguistic term or distribution assessments of a linguistic term set. We first discuss the conditions of obtaining the complete trust network, and the propagation and aggregation of the trust relationships with a single linguistic term. Since the linguistic term set may be symmetric and uniform, symmetric and non-uniform, or asymmetric and non-uniform, we translate linguistic terms into numerical indexes and define the propagation operator based on the semantics of the linguistic term and the Archimedean t-norm. The propagation result is translated to 2−tuple linguistic model because it may not exist in the initial linguistic term set. Some properties are proposed to verify that the proposed operator is compatible with human thought. Then the 2−tuple distribution assessments on a linguistic term set are defined, and the other aggregation operator is proposed to propagate linguistic distribution assessment trust relationships. The second aggregation operator focuses on both the aggregation of linguistic terms and symbolic proportions of linguistic terms and is a generalization of the first operator. Finally, a numerical example of CouchSurfing comparative analyses further demonstrates that the proposed operators are effective and reasonable, and can consider the different semantics of a linguistic term in practical application.

信任网络分析已广泛应用于各个领域，如群体推荐、群体决策等相关领域。在本文中，我们专注于获得完整的信任网络，在该网络中，专家用单个语言术语或语言术语集的分布评估来表达他们对另一个人的信任关系。我们首先讨论获得完整信任网络的条件，以及使用单个语言术语进行信任关系的传播和聚合。由于语言术语集可能是对称和均匀的、对称和非均匀的、或不对称和非均匀的，我们将语言术语翻译成数字索引，并根据语言术语的语义和阿基米德t范数定义传播算子。传播结果被转换为 2−元组语言模型，因为它可能不存在于初始语言术语集中。提出了一些属性来验证所提出的算子是否与人类思维兼容。然后定义语言术语集上的 2−元组分布评估，并提出另一个聚合算子来传播语言分布评估信任关系。第二个聚合算子关注语言术语的聚合和语言术语的符号比例，并且是第一个算子的概括。最后，通过CouchSurfing的数值算例进行对比分析，进一步证明所提出的算子是有效和合理的，并且在实际应用中能够考虑语言术语的不同语义。