Identify nodes with high spreader power in social networks by considering the positive and negative influence weights

Social networks have found a significant place in society. The large number of users of these networks and the high volume of data that is exchanged through these networks daily have made these networks a good opportunity for advertising companies. Due to the budget constraints, companies are forced to select only a limited number of users to begin the propagation process. In order to be successful in advertising and informing the majority of the community, the selected collection must be selected among influential people. Many studies have addressed the issue of selecting the optimal subset of influential people. But in the majority of them, it is assumed that the influence between people is positive and the negative influence of people on each other is not considered. Therefore, in this study, a method proposed for selecting the optimal subset of influential people according to the positive and negative influences between individuals. In the proposed method, first for each member of the community, a set of positive and negative influences of one and two steps is obtained. Then, using multi-criteria decision making method, a method for selecting the best people is presented. The Wikipedia data set was used for the evaluations. The results obtained for the spreading power of the set selected by the proposed method and other methods indicate that although in some cases some methods have more positive propagation than the proposed method, but the proposed method in all Scenarios has a much better performance in terms of negative emission.