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An Efficient Influence Maximization Algorithm Based on Social Relationship Priority in Mobile Social Networks

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Book cover Security and Privacy in Social Networks and Big Data (SocialSec 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1298))

Abstract

The mobile social network (MSN) combines techniques in social science and wireless communications for mobile networking. The MSN can be considered as a system which provides a variety of data delivery services involving the social relationship among mobile users. The key problem in MSNs is Influence Maximization (IM), which aims at finding the top-k influential users from the mobile social network and contributing to the spread of maximum information, the users may have different attitudes (positive/negative) towards a message when the message appears in MSNs. In this paper, we first model the mobile social network as the topological graph based on social priority topological to study the social influence. Then we innovatively propose a scheme which integrates ITÖ algorithm into PSO algorithm to solve the problem of maximizing the influence in MSNs. Finally, experimental evaluation shows that the scheme we proposed to identify influential nodes is more accurate and efficient than other schemes by comparison, and the probability of maximizing the influence of our scheme can reach to \(56\%\).

Supported by National Natural Science Foundation of China.

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Acknowledgement

The authors would like to thank the National Science Foundation of China (Nos. U1905211, 61771140, 61702100, 61702103).

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Authors and Affiliations

  1. College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian, China

    Xinxin Zhang, Li Xu & Min Gao

  2. Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou, Fujian, China

    Xinxin Zhang, Li Xu & Min Gao

Authors
  1. Xinxin Zhang
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  2. Li Xu
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  3. Min Gao
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Corresponding author

Correspondence to Li Xu .

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Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Yang Xiang

  2. Nankai University, Tianjin, China

    Zheli Liu

  3. Guangzhou University, Guangzhou, China

    Jin Li

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Zhang, X., Xu, L., Gao, M. (2020). An Efficient Influence Maximization Algorithm Based on Social Relationship Priority in Mobile Social Networks. In: Xiang, Y., Liu, Z., Li, J. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2020. Communications in Computer and Information Science, vol 1298. Springer, Singapore. https://doi.org/10.1007/978-981-15-9031-3_15

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  • DOI: https://doi.org/10.1007/978-981-15-9031-3_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9030-6

  • Online ISBN: 978-981-15-9031-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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