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.
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.
Identifying influential nodes is a fundamental and open issue in analysis of the complex networks. The measurement of the spreading capabilities of nodes is an attractive challenge in this field. Node centrality is one of the most popular methods used to identify the influential nodes, which includes the degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC). The DC is an efficient method but not effective. The BC and CC are effective but not efficient. They have high computational complexity. To balance the effectiveness and efficiency, this paper proposes the neighborhood entropy centrality to rank the influential nodes. The proposed method uses the notion of entropy to improve the DC. For evaluating the performance, the susceptible-infected-recovered model is used to simulate the information spreading process of messages on nine real-world networks. The experimental results reveal the accuracy and efficiency of the proposed method.
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