Source localization in signed networks based on dynamic message passing algorithm

Abstract

Sign characteristics of social networks play a crucial role in the process of information dissemination, and structural balance theory is a significant property of signed networks. Despite some progress in source localization, the sign features of network structures are widely overlooked. Furthermore, the impact of structural balance on tracing remains unclear. In this paper, we explore a model of information propagation in signed networks and investigate source localization in the context of a given network state snapshot. We first propose a modified signed susceptibility-infection-recovery (S-SIR) propagation model, combining the positive/negative transmission rates based on structural balance theory, which is designed to better approximate the real-world social network structure. Next, a signed dynamic message passing algorithm (S-DMP) is proposed for source localization. Simulation experiments show that the S-DMP algorithm can fully utilize both the positive and negative attributes of the edges in the signed network and outperforms other methods in terms of source localization accuracy. These experiments prove that the proposed method can be used for source localization in the early stage of information transmission. Furthermore, we find that the accuracy of source localization is highest when there is a balance between the number of positive and negative edges.