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Maximizing influence in a social network: Improved results using a genetic algorithm

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  • Zhang, Kaiqi
  • Du, Haifeng
  • Feldman, Marcus W.

Abstract

The influence maximization problem focuses on finding a small subset of nodes in a social network that maximizes the spread of influence. While the greedy algorithm and some improvements to it have been applied to solve this problem, the long solution time remains a problem. Stochastic optimization algorithms, such as simulated annealing, are other choices for solving this problem, but they often become trapped in local optima. We propose a genetic algorithm to solve the influence maximization problem. Through multi-population competition, using this algorithm we achieve an optimal result while maintaining diversity of the solution. We tested our method with actual networks, and our genetic algorithm performed slightly worse than the greedy algorithm but better than other algorithms.

Suggested Citation

  • Zhang, Kaiqi & Du, Haifeng & Feldman, Marcus W., 2017. "Maximizing influence in a social network: Improved results using a genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 20-30.
    • Handle: RePEc:eee:phsmap:v:478:y:2017:i:c:p:20-30
    DOI: 10.1016/j.physa.2017.02.067
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    References listed on IDEAS

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    1. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
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    Cited by:

    1. Altay, Elif Varol & Alatas, Bilal, 2020. "Randomness as source for inspiring solution search methods: Music based approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    2. Fei Ye & Xinxiu Xie & Li Zhang & Xiaoling Hu, 2018. "An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China," Energies, MDPI, vol. 11(1), pages 1-16, January.
    3. Shang, Jiaxing & Wu, Hongchun & Zhou, Shangbo & Zhong, Jiang & Feng, Yong & Qiang, Baohua, 2018. "IMPC: Influence maximization based on multi-neighbor potential in community networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1085-1103.
    4. Varol Altay, Elif & Alatas, Bilal, 2020. "Intelligent optimization algorithms for the problem of mining numerical association rules," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    5. Jabari Lotf, Jalil & Abdollahi Azgomi, Mohammad & Ebrahimi Dishabi, Mohammad Reza, 2022. "An improved influence maximization method for social networks based on genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

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