Influence maximization plays an important role in social network analysis and has been extensively explored due to its emergence in a great deal of applications. However, a vast majority of researchers have only considered the influence of one factor on the spread of information among individuals. In addition, this factor which is mainly the influence probability, has been considered to be fixed which is estimated based on the number of neighbors of each individual. While, in reality, we do not have complete information about this probability and it is not a fixed value. Moreover, in the spread of information, other factors including trust, are also involved, which cannot be easily combined due to their different nature. In this paper, a dominance approach to influence maximization is studied under the condition, in which, complete information about influence of users on each other is not available and users infect each other based on an unknown influence probability. Furthermore, in order to make the proposed model closer to reality, trust factor is also considered as an independent factor. The proposed method is compared to several state-of-the-art influence maximization algorithms and attained results on several synthetic and real social network datasets verify effectiveness of the proposed method compared to previous methods in the context of influence maximization.
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