Using Social Sensors for Influence Propagation in Networks with Positive and Negative Relationships

Online social communities often exhibit complex relationship structures, ranging from close friends to political rivals. As a result, persons are influenced by their friends and foes differently. Future network applications can benefit from integrating these structural differences in propagation schemes through socially aware sensors. In this paper, we introduce a propagation model for such social sensor networks with positive and negative relationship types. We tackle two main scenarios based on this model. The first one is to minimize the end-to-end propagation cost of influencing a target person in favor of an idea by utilizing sensor observations about the relationship types in the underlying social graph. The propagation cost is incurred by social and physical network dynamics such as propagation delay, frequency of interaction, the strength of friendship/foe ties or the impact factor of the propagating idea. We next extend this problem by incorporating the impact of message deterioration and ignorance, and by limiting the number of persons influenced against the idea before reaching the target. Second, we study the propagation problem while minimizing the number of negatively influenced persons on the path, and provide extensions to elaborate on the impact of network parameters. We demonstrate our results in both an artificially created network and the Epinions signed network topology. Our results show that judicious propagation schemes lead to a significant reduction in the average cost and complexity of network propagation compared to naïve myopic algorithms.