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Meta-heuristic algorithms for influence maximization: a survey

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Abstract

Influence maximization (IM) is a key problem in social network analysis, which has attracted attention of many scholars due to the wide range of applications, the variety of IM algorithms have been proposed from different perspectives. In this paper, we review IM algorithms from the perspective of meta-heuristic optimization, proposed a two-layer structure taxonomy to organize almost all the meta-heuristic IM algorithms. The initial layer, predicated upon the delineation of problem construction models, stratifies IM algorithms into two categories: single-objective and multi-objective IM algorithms. Subsequently, the secondary layer discerns between evolution-based and population intelligence-based IM algorithms, delineating them according to the underlying conceptual frameworks, a detailed exposition and analysis ensue. Subsequent scrutiny involves an exhaustive evaluation of the merits and demerits inherent in each IM algorithm, juxtaposing considerations such as time complexity and experimental validation methodologies. Furthermore, we distill myriad strategies aimed at enhancing accuracy and mitigating time complexity across the four phases of the algorithmic process. Finally, based on the above analysis, the challenges and future directions of IM problems are outlined from the perspective of algorithms, applications and models.

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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61876186), the Project of Xuzhou Science and Technology (No.KC21300), and funded by the China Scholarship Council.

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Authors and Affiliations

  1. Department of Computer Science, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Chencheng Fan, Zhixiao Wang, Jian Zhang & Jiayu Zhao

  2. Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou, 221116, Jiangsu, China

    Zhixiao Wang

  3. School of Physical Education, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Xianfeng Meng

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Fan, C., Wang, Z., Zhang, J. et al. Meta-heuristic algorithms for influence maximization: a survey. Evolving Systems 16, 24 (2025). https://doi.org/10.1007/s12530-024-09640-2

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