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Influence Maximization in Social Networks

In: Optimization in Large Scale Problems

Author

Listed:
  • Shashank Sheshar Singh

    (Indian Institute of Technology (BHU))

  • Ajay Kumar

    (Indian Institute of Technology (BHU))

  • Shivansh Mishra

    (Indian Institute of Technology (BHU))

  • Kuldeep Singh

    (Indian Institute of Technology (BHU))

  • Bhaskar Biswas

    (Indian Institute of Technology (BHU))

Abstract

Influence maximization (IM) is the problem of identifying a small subset of influential users such that influence spread in a network can be maximized. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing, election campaign, counter-terrorism efforts, rumor control, and sales promotions, etc. In this paper, we perform a comparative review of the existing IM algorithms. First, we present a comprehensive study on existing IM approaches with their comparative theoretical analysis. Then, we present a comparative analysis of existing IM methods with respect to performance metrics. Finally, we discuss the challenges and future directions of the research.

Suggested Citation

  • Shashank Sheshar Singh & Ajay Kumar & Shivansh Mishra & Kuldeep Singh & Bhaskar Biswas, 2019. "Influence Maximization in Social Networks," Springer Optimization and Its Applications, in: Mahdi Fathi & Marzieh Khakifirooz & Panos M. Pardalos (ed.), Optimization in Large Scale Problems, pages 255-267, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-28565-4_22
    DOI: 10.1007/978-3-030-28565-4_22
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    Citations

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    Cited by:

    1. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    2. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    3. Alexander Tselykh & Vladislav Vasilev & Larisa Tselykh & Fernando A. F. Ferreira, 2022. "Influence control method on directed weighted signed graphs with deterministic causality," Annals of Operations Research, Springer, vol. 311(2), pages 1281-1305, April.
    4. Lin Zhang & Kan Li, 2021. "Influence Maximization Based on Backward Reasoning in Online Social Networks," Mathematics, MDPI, vol. 9(24), pages 1-17, December.
    5. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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