Abstract
Influence maximization is to find a small number of seed nodes in the network that maximize their influence on the network. Existing algorithms select a seed node with the greatest influence. This will inevitably have an influence on mutual coverage, which will have a more or less negative impact on the final results and reduce the performance of the algorithm. In this paper, Node Diffusibility is proposed, and it is updated in real time and eliminated the deviation caused by its overlay. On the basis of traditional calculation of node influence, more attention was paid to the influence of a node’s neighboring nodes rather than to the characteristics of the nodes themselves. The proposed algorithm was evaluated by experiments conducted on selected real data sets. Compared with the classical ranking-based algorithms, MaxDegree and PageRank, the proposed algorithm achieved better results in terms of efficiency and time complexity.
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The research presented in this paper is supported by the National Key R&D Program of China (No. 2017YFE0117500) and the National Natural Science Foundation of China (No. 61762002).
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Ren, Y., Zhang, X., Xia, L., Lin, Y., Zhao, Y., Li, W. (2019). An Influence Maximization Algorithm Based on Real-Time and De-superimposed Diffusibility. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_37
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