One of the main problems in viral marketing is influence maximization (IM). With a social network and a predefined propagation model, the aim is to seek a subset of nodes that spread the influence widely into the network. Most scalable methods with provable approximation guarantees are presented for this problem based on the reverse influence sampling (RIS) framework. The RIS framework has two phases: sampling and node selection. The sampling phase encountered two challenges in the sampling phase: the number of required samples and the sampling method. Most methods have focused on the first challenge, that is, sample size, and have tried to provide a rigid sample size. In this paper, we focus on the second challenge: how to improve the precision of sampling. We propose to use stratified sampling rather than simple random sampling. Since the degree of each node is one of the affecting factors in the diffusion process. This issue leads us to use stratified sampling based on a degree distribution. The results show that with the application of the proposed method, the solution can estimate with fewer samples, which is faster than the state- of-the-art methods.
