Liars are More Influential: Effect of Deception in Influence Maximization on Social Networks

Detecting influential users, called the influence maximization problem on social networks, is an important graph mining problem with many diverse applications such as information propagation, market advertising, and rumor controlling. There are many studies in the literature on the influential user detection problem in social networks. Although the current methods are successfully used in many different applications, they typically operate under the assumption that users are honest with one another, overlooking the role of deception in these environments. On the other hand, deception appears to be surprisingly common among humans within social networks. In this paper, we study the effect of deception in influence maximization on social networks. We begin by modeling deception within social networks and then explore opinion dynamics in these networks, incorporating deception through a recent opinion dynamics framework based on the sheaf Laplacian. We propose two methods for detecting influential nodes: sheaf Laplacian centrality and sheaf DFF centrality, both of which are designed to assess the influence of deception in the context of influence maximization. Our experimental results on synthetic and realworld networks suggest that liars are more influential than honest users in social networks. Our study underscores the potential danger and implications of deceptive practices in social network contexts.