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An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks

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  • Lin, Geng
  • Guan, Jian
  • Feng, Huibin

Abstract

The positive influence dominating set problem is a variant of the minimum dominating set problem, and has lots of applications in social networks. It is NP-hard, and receives more and more attention. Various methods have been proposed to solve the positive influence dominating set problem. However, most of the existing work focused on greedy algorithms, and the solution quality needs to be improved. In this paper, we formulate the minimum positive influence dominating set problem as an integer linear programming (ILP), and propose an ILP based memetic algorithm (ILPMA) for solving the problem. The ILPMA integrates a greedy randomized adaptive construction procedure, a crossover operator, a repair operator, and a tabu search procedure. The performance of ILPMA is validated on nine real-world social networks with nodes up to 36,692. The results show that ILPMA significantly improves the solution quality, and is robust.

Suggested Citation

  • Lin, Geng & Guan, Jian & Feng, Huibin, 2018. "An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 199-209.
    • Handle: RePEc:eee:phsmap:v:500:y:2018:i:c:p:199-209
    DOI: 10.1016/j.physa.2018.02.119
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    References listed on IDEAS

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    Cited by:

    1. S. Raghavan & Rui Zhang, 2022. "Rapid Influence Maximization on Social Networks: The Positive Influence Dominating Set Problem," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1345-1365, May.
    2. Weidong Chen & Hao Zhong & Lidong Wu & Ding-Zhu Du, 2022. "A general greedy approximation algorithm for finding minimum positive influence dominating sets in social networks," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 1-20, August.
    3. Lei Gao & Dong Han, 2020. "Extreme Value Distributions for Two Kinds of Path Sums of Markov Chain," Methodology and Computing in Applied Probability, Springer, vol. 22(1), pages 279-294, March.
    4. S. Raghavan & Rui Zhang, 2022. "Influence Maximization with Latency Requirements on Social Networks," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 710-728, March.

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