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Improved Evolution Algorithm that Guides the Direction of Individual Mutation for Influence Maximization in Social Networks

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Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12817))

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Abstract

The purpose of influence maximization in social networks is to find K nodes as the spread source to activate as many nodes as possible. To improve the efficiency and effectiveness of the classic genetic algorithm in large social networks, the diffusion evaluation function is first proposed to estimate the impact range of seed nodes. Then the individuals are initialized based on the diffusion degree centrality of the node. Adapted a crossover strategy is used to help the evolution algorithm to achieve the purpose of local search. Besides, a direction vector is designed to guide the individual’s mutation. Through experiments on real social networks, the improved evolution algorithm can approximate the state-of-the-art greedy algorithm in the final result while also significantly improving time efficiency.

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Xiaoqin Tang & Xuxing Liu

Authors
  1. Xiaoqin Tang
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  2. Xuxing Liu
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Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Han Qiu

  2. Ibaraki University, Hitachi, Japan

    Cheng Zhang

  3. University of Kentucky, Lexington, KY, USA

    Zongming Fei

  4. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

  5. Princeton University, Princeton, NJ, USA

    Sun-Yuan Kung

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Tang, X., Liu, X. (2021). Improved Evolution Algorithm that Guides the Direction of Individual Mutation for Influence Maximization in Social Networks. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_44

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  • DOI: https://doi.org/10.1007/978-3-030-82153-1_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82152-4

  • Online ISBN: 978-3-030-82153-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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