TSIM: A Two-Stage Selection Algorithm for Influence Maximization in Social Networks

The influence maximization problem is aimed at finding a small subset of nodes in a social./network to maximize the expected number of nodes influenced by these nodes. Influence maximization plays an important role in viral marketing and information diffusion. However, some existing algorithms for influence maximization in social networks perform badly in either efficiency or accuracy. In this paper, we put forward an efficient algorithm, called a two-stage selection for influence maximization in social networks (TSIM). Moreover, a discount-degree descending technology and lazy-forward technology are proposed, called DDLF, to select a certain number of influential nodes as candidate nodes. Firstly, we utilize the strategy to select a certain number of nodes as candidate nodes. Secondly, this paper proposes the maximum influence value function to estimate the marginal influence of each candidate node. Finally, we select seed nodes from candidate nodes according to their maximum influence value. The experimental results on six real-world social networks show that the proposed algorithm outperforms other contrast algorithms while considering accuracy and efficiency comprehensively.