Abstract
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.
Highlights
With development and popularity of internet technology, more and more people make a connection with other people by social networks [1]–[3], which promotes the birth of various social networks, such as communication networks, collaboration networks [4], mobile social networks [5], [6] and online social networks [7], [8] etc
(2) A new algorithm called Global Selection Based on Local Influence is proposed in this paper, which utilizes the two-stage filtering strategy and submodular property to overcome low efficiency of greedy-based algorithms
FRAMEWORK OF GLOBAL SELECTION BASED ON LOCAL INFLUENCE This paper proposes a new algorithm called Global Selection Based on Local Influence for influence maximization in social networks
Summary
With development and popularity of internet technology, more and more people make a connection with other people by social networks [1]–[3], which promotes the birth of various social networks, such as communication networks, collaboration networks [4], mobile social networks [5], [6] and online social networks [7], [8] etc. Different from the above approaches, Wang et al [17] propose an algorithm called CGA, which utilizes community to solve influence maximization problem This method improves computation efficiency by sacrificing result accuracy. In 2015, Tang et al [20] propose a novel algorithm called IMM, which utilizes a martingale approach to obtain accurate results in near-linear time This approach improves computation efficiency by sacrificing large accuracy, which is unsatisfactory. We propose a new algorithm for influence maximization problem This algorithm attempts to make a suitable compromise between computation efficiency and result accuracy by the two-stage filtering strategy of candidate nodes and a new objective function. (2) A new algorithm called Global Selection Based on Local Influence is proposed in this paper, which utilizes the two-stage filtering strategy and submodular property to overcome low efficiency of greedy-based algorithms.
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