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Influence maximization in social networks based on discrete harris hawks optimization algorithm

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Abstract

Influence Maximization (IM) is an important topic in the field of social network analysis, and is widely used in viral marketing, recommendation systems, rumor prevention and other fields. The meta-heuristic method has excellent performance because of high scalability and low complexity, however, the objective function of existing meta-heuristic methods can only be applied to propagation models with small probabilities. In addition, there is room for further improvement in performance of meta-heuristic methods. In order to solve the above problems, this paper transforms the influence maximization problem into an optimization problem and designs a novel objective function based on the six degrees of separation theory of social networks. Then, with the designed objective function, this paper discretizes the harris hawks optimization (HHO) algorithm by redefining the energy and location representation rules for influence maximization problem. Experimental results on eight real datasets demonstrate that the proposed objective function exhibits high accuracy and generality, suitable for various probability propagation models. With the exploration and exploitation process in steps, dynamically using different strategies various situations, the proposed DHHO algorithm exhibits better performance in dealing with influence maximization problem, outperforming the state-of-the-art methods.

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Data availability

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), the Graduate Innovation Program of China University of Mining and Technology (No.2023WLKXJ177), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No.KYCX23\(\_\)2726), and the Fundamental Research Funds for the Central Universities (No.2022QN1093).

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Authors and Affiliations

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

    Chencheng Fan, Zhixiao Wang, Jian Zhang, Jiayu Zhao & Xiaobin Rui

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

    Zhixiao Wang & Xiaobin Rui

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  1. Chencheng Fan
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  2. Zhixiao Wang
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  3. Jian Zhang
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Correspondence to Zhixiao Wang.

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Fan, C., Wang, Z., Zhang, J. et al. Influence maximization in social networks based on discrete harris hawks optimization algorithm. Computing 106, 327–351 (2024). https://doi.org/10.1007/s00607-023-01207-4

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