Influence maximization based on SATS scheme in social networks

Key user identification for messages propagation constitutes one of the most important topics in social networks. The success of the information spreading depends on the selection of the key users. Besides the social attributes of users, the topology structure of the networks should be considered in this mechanism to ensure performance. In this paper, we propose the Social Attributes and Topology Structure (SATS) scheme for discovering key users in social networks. Firstly, the proposed SATS scheme utilizes four types of social attributes, and we extract the key social attributes. Then, we explore the feature of topology structure, in which social influence is defined and top-k influential users can be identified. Finally, the experiment results show that the proposed SATS scheme has effective propagation ability in the Susceptible Infected Recovered model, we can find the nodes which have more effects on network robustness while encountering the intentional attack by SATS scheme, and the proposed SATS scheme achieves better performance in identifying key users compared with some baseline approaches.