It is generally accepted that trust is one of the main issues in the area of social networks. Trust values can be measured explicitly or implicitly. There are various approaches to compute the trust value of users implicitly. These approaches consider a uniform trusting behavior for all users of a social network. While it would be more accurate to acknowledge the differences between the users in terms of how they trust others and the factors that they consider in this regard. Trusting behavior of users can also be influenced by the behavior of others with whom they interact. The mechanism of these influences and the conditions for changing the trusting behavior are of great importance for this discussion. In this study, our goal is to model the differences between the trusting behaviors of social network users. For this purpose, we define three behavior modes for the way users trust each other. In each mode, trust calculations are based on behavioral and functional characteristics of users, which are shaped by their subjective beliefs. A dataset that has the interaction information between each pair of nodes is used to define the trusting behavior pattern between users. Also, three scenarios are defined for assessing the propagation of the behavior modes. Then, we attempt to maximize the influence and find the effective nodes for propagating the trusting behavior modes in the network. To this end, we focus on the structure of the social network users in different propagation scenarios. The results show differences between the trust level outcomes reached with each behavior mode. Also, the impacts of propagation source node selection methods differ in each propagation scenario.

人们普遍认为，信任是社交网络领域的主要问题之一。信任值可以显式或隐式测量。有多种方法可以隐式计算用户的信任值。这些方法为社交网络的所有用户考虑了统一的信任行为。虽然承认用户之间在他们如何信任他人以及他们在这方面考虑的因素方面的差异会更准确。用户的信任行为也可能受到与他们交互的其他人的行为的影响。这些影响的机制和改变信任行为的条件对于本次讨论非常重要。在这项研究中，我们的目标是对社交网络用户的信任行为之间的差异进行建模。以此目的，我们为用户相互信任的方式定义了三种行为模式。在每种模式中，信任计算都是基于用户的行为和功能特征，这些特征是由他们的主观信念塑造的。包含每对节点之间交互信息的数据集用于定义用户之间的信任行为模式。此外，定义了三个场景来评估行为模式的传播。然后，我们尝试最大化影响并找到在网络中传播信任行为模式的有效节点。为此，我们重点研究了不同传播场景下社交网络用户的结构。结果显示了每种行为模式达到的信任级别结果之间的差异。还，