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Adaptive Influence Maximization: Adaptability via Nonadaptability

Author

Listed:
  • Hongmin W. Du

    (Accounting and Information Systems Department, Rutgers University, Piscataway, New Jersey 08854)

  • Yingfan L. Du

    (Department of Finance, University of Texas, Austin, Texas 78712)

  • Zhao Zhang

    (School of Mathematical Sciences, Zhejiang Normal University, Jinhua, Zhejiang 321004, China)

Abstract

Adaptive influence maximization is an important research problem in computational social networks, which is also a typical problem in the study of adaptive processing of information and adaptive construction of objects. In this paper, we propose a new method that reduces the adaptive influence maximization problem into a nonadaptive one in a different social network, so that an adaptive optimization can be solved by those methods for nonadaptive optimization. In addition, we provide a new approximation algorithm for the submodular maximization problem with a knapsack constraint, which runs in O ( n 2 ) time and has performance ratio 1 − 1 / e , where n is the number of nodes in the network. The ratio is better than the best known previous one with the same running time.

Suggested Citation

  • Hongmin W. Du & Yingfan L. Du & Zhao Zhang, 2024. "Adaptive Influence Maximization: Adaptability via Nonadaptability," INFORMS Journal on Computing, INFORMS, vol. 36(5), pages 1190-1200, September.
    • Handle: RePEc:inm:orijoc:v:36:y:2024:i:5:p:1190-1200
    DOI: 10.1287/ijoc.2023.0267
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