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Research on the Influence Maximization Problem in Social Networks Based on the Multi-Functional Complex Networks Model

Research on the Influence Maximization Problem in Social Networks Based on the Multi-Functional Complex Networks Model

Sheng Bin, Gengxin Sun
Copyright: © 2022 |Volume: 34 |Issue: 3 |Pages: 17
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799893264|DOI: 10.4018/JOEUC.302662
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MLA

Bin, Sheng, and Gengxin Sun. "Research on the Influence Maximization Problem in Social Networks Based on the Multi-Functional Complex Networks Model." JOEUC vol.34, no.3 2022: pp.1-17. http://doi.org/10.4018/JOEUC.302662

APA

Bin, S. & Sun, G. (2022). Research on the Influence Maximization Problem in Social Networks Based on the Multi-Functional Complex Networks Model. Journal of Organizational and End User Computing (JOEUC), 34(3), 1-17. http://doi.org/10.4018/JOEUC.302662

Chicago

Bin, Sheng, and Gengxin Sun. "Research on the Influence Maximization Problem in Social Networks Based on the Multi-Functional Complex Networks Model," Journal of Organizational and End User Computing (JOEUC) 34, no.3: 1-17. http://doi.org/10.4018/JOEUC.302662

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Abstract

Most of the existing influence maximization problem in social networks only focus on single relationship social networks, that is, there is only one relationship in social networks. However, in reality, there are often many relationships among users of social networks, and these relationships jointly affect the propagation of network information and its final scope of influence. Based on the classical linear threshold model and combined with various relationships between network nodes, in this paper MRSN-LT propagation model is proposed to model the influence propagation process between nodes in multiple relationships social networks. Then, MRSN-RRset algorithm based on reverse reachable set is proposed to solve the problem of low computational performance caused by greedy algorithm in the research process of traditional influence maximization. Finally, the experimental results on real data sets show that the proposed method has better influence propagation scope and greater computational performance improvement.