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A depth-based heuristic to solve the multi-objective influence spread problem using particle swarm optimization

Author

Listed:
  • Fabián Riquelme

    (Universidad de Valparaíso)

  • Francisco Muñoz

    (Universidad de Valparaíso)

  • Rodrigo Olivares

    (Universidad de Valparaíso)

Abstract

The influence spread in a social network is an iterative process that can take several steps. It begins with an activation seed and finishes when the current activation cannot influence more actors. The multi-objective influence spread problem corresponds to finding the smallest number of actors capable of maximizing the influence spread within the network. This problem has been solved by metaheuristic optimization algorithms using swarm intelligence methods. This article proposes a heuristic to improve the existing solution: when two sets of actors can influence the same number of actors, the one whose spread requires the least number of steps is chosen. The proposed solution is tested on two different real networks. The results show that the heuristic allowed better results for both networks and decreased the average number of steps in the influence spread processes (in 15.5 and 0.07 average steps, respectively), thus improving execution times. Moreover, the heuristic allowed decreasing the number of steps in 83% (against 17% of increasing) and 13% (against 7% of increasing) of the particles, respectively.

Suggested Citation

  • Fabián Riquelme & Francisco Muñoz & Rodrigo Olivares, 2023. "A depth-based heuristic to solve the multi-objective influence spread problem using particle swarm optimization," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1267-1285, September.
    • Handle: RePEc:spr:opsear:v:60:y:2023:i:3:d:10.1007_s12597-023-00662-z
    DOI: 10.1007/s12597-023-00662-z
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    References listed on IDEAS

    as
    1. Xionghua Huang & Tiaojun Zeng & MinSong Li & Ching-Feng Wen, 2022. "A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy," Journal of Mathematics, Hindawi, vol. 2022, pages 1-13, November.
    2. Nicolás Caselli & Ricardo Soto & Broderick Crawford & Sergio Valdivia & Rodrigo Olivares, 2021. "A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique," Mathematics, MDPI, vol. 9(16), pages 1-28, August.
    3. Sergio Valdivia & Ricardo Soto & Broderick Crawford & Nicolás Caselli & Fernando Paredes & Carlos Castro & Rodrigo Olivares, 2020. "Clustering-Based Binarization Methods Applied to the Crow Search Algorithm for 0/1 Combinatorial Problems," Mathematics, MDPI, vol. 8(7), pages 1-42, July.
    4. Riquelme, Fabián & Gonzalez-Cantergiani, Pablo & Molinero, Xavier & Serna, Maria, 2019. "The neighborhood role in the linear threshold rank on social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
    5. Molinero, Xavier & Riquelme, Fabián & Serna, Maria, 2015. "Cooperation through social influence," European Journal of Operational Research, Elsevier, vol. 242(3), pages 960-974.
    6. Broderick Crawford & Ricardo Soto & Eric Monfroy & Carlos Castro & Wenceslao Palma & Fernando Paredes, 2013. "A Hybrid Soft Computing Approach for Subset Problems," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-12, July.
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