Dynamic social feature-based diffusion in mobile social networks

With the wide use of smart mobile devices and the popularity of mobile social networks (MSNs), direct marketing has been adopted by more and more companies to announce the news of their products first to a group of selected profitable customers and let them diffuse the news by "word-of-mouth" to other potential buyers to control the marketing cost. In this paper, we study the diffusion minimization problem whose goal is to select an optimal set of initial nodes to disseminate the information to the whole network as quickly as possible. We tackle the problem by taking advantage of node social features in MSNs. We define dynamic social features to capture nodes´ dynamic contact behavior and use social similarity metrics to measure their social closeness. We adopt the community concept in social networks to reduce the complexity of the diffusion minimization problem. We propose novel diffusion node selection algorithms based on these new features to minimize the diffusion time. Simulation results show that our algorithms have lower diffusion times than the existing ones.