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Influence maximization is to select k nodes from social networks to maximize the expected number of nodes activated by these selected nodes. Influence maximization problem plays a vital role in commercial marketing, news propagation, rumor control and public services. However, the existing algorithms for influence maximization usually tend to select one aspect from efficiency and accuracy as its main improving objective. This method of excessively pursuing one metric often leads to performing poorly in other metrics. Hence, we think that algorithms for influence maximization should make a suitable compromise between computation efficiency and result accuracy instead of excessively pursuing for one metric. Based on the above understanding, this paper proposes a new algorithm, called Global Selection Based on Local Influence (LGIM). The basic idea of the proposed algorithm is following: if a node can influence another node with large influence, the node also has large influence. Therefore, a two-stage filtering strategy of candidate nodes is proposed, which can reduce a large number of running time. Moreover, this paper also proposes a new objective function to estimate the influence spread of a node set. In summarize, the proposed algorithm utilizes the two-stage filtering strategy of candidate nodes to avoid unnecessary computation, and adopts a new objective function to replace time-consuming Monte-Carle simulations. Experimental results on six real-world social networks demonstrate that the proposed algorithm outperforms other four comparison algorithms when comprehensively considering computation efficiency and result accuracy.

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