Finding effective nodes to maximize the trusting behavior propagation in social networks

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.