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An exact method for influence maximization based on deterministic linear threshold model

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  • Eszter Julianna Csókás

    (University of Szeged)

  • Tamás Vinkó

    (University of Szeged)

Abstract

Influence maximization (IM) is a challenging combinatorial optimization problem on (social) networks given a diffusion model and limited choice for initial seed nodes. In a recent paper by Keskin and Güler (Turkish J of Electrical Eng & Comput Sci 26:3383–3396, 2018) an integer programming formalization of IM using the so-called deterministic linear threshold diffusion model was proposed. In fact, it is a special 0-1 linear program in which the objective is to maximize influence while minimizing the diffusion time. In this paper, by rigorous analysis, we show that the proposed algorithm can get stuck in locally optimal solution or cannot even start on certain input graphs. The identified problems are resolved by introducing further constraints which then leads to a correct algorithmic solution. Benchmarking results are shown to demonstrate the efficiency of the proposed method.

Suggested Citation

  • Eszter Julianna Csókás & Tamás Vinkó, 2023. "An exact method for influence maximization based on deterministic linear threshold model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 269-286, March.
    • Handle: RePEc:spr:cejnor:v:31:y:2023:i:1:d:10.1007_s10100-022-00807-3
    DOI: 10.1007/s10100-022-00807-3
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    References listed on IDEAS

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    1. Kahr, Michael & Leitner, Markus & Ruthmair, Mario & Sinnl, Markus, 2021. "Benders decomposition for competitive influence maximization in (social) networks," Omega, Elsevier, vol. 100(C).
    2. Güney, Evren & Leitner, Markus & Ruthmair, Mario & Sinnl, Markus, 2021. "Large-scale influence maximization via maximal covering location," European Journal of Operational Research, Elsevier, vol. 289(1), pages 144-164.
    3. Hao-Hsiang Wu & Simge Küçükyavuz, 2018. "A two-stage stochastic programming approach for influence maximization in social networks," Computational Optimization and Applications, Springer, vol. 69(3), pages 563-595, April.
    4. Zaixin Lu & Wei Zhang & Weili Wu & Joonmo Kim & Bin Fu, 2012. "The complexity of influence maximization problem in the deterministic linear threshold model," Journal of Combinatorial Optimization, Springer, vol. 24(3), pages 374-378, October.
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