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Adaptive seeding for profit maximization in social networks

Author

Listed:
  • Chuangen Gao

    (Qilu Technology University)

  • Shuyang Gu

    (Texas A&M University)

  • Jiguo Yu

    (Qilu Technology University)

  • Hai Du

    (Shaanxi Normal University)

  • Weili Wu

    (University of Texas at Dallas)

Abstract

Social networks are becoming important dissemination platforms, and a large body of works have been performed on viral marketing, but most are to maximize the benefits associated with the number of active nodes. In this paper, we study the benefits related to interactions among activated nodes. Furthermore, since the stochasticity caused by the dynamics of influence cascade in the social network, we propose the adaptive seeding strategy where seeds are selected one by one according to influence propagation situation of seeds already selected, and define the adaptive profit maximization problem. We analyze its complexity and prove it is not adaptive submodular. We find the upper and lower bounds which are adaptive submodular and design an adaptive sandwich policy based on the sandwich strategy which could gain a data dependent approximation solution. Through real data sets, we verify the effectiveness of our proposed algorithm.

Suggested Citation

  • Chuangen Gao & Shuyang Gu & Jiguo Yu & Hai Du & Weili Wu, 2022. "Adaptive seeding for profit maximization in social networks," Journal of Global Optimization, Springer, vol. 82(2), pages 413-432, February.
    • Handle: RePEc:spr:jglopt:v:82:y:2022:i:2:d:10.1007_s10898-021-01076-1
    DOI: 10.1007/s10898-021-01076-1
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    References listed on IDEAS

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    Cited by:

    1. Jiachen Ju & Xiao Wang & Dachuan Xu, 2024. "Online non-monotone diminishing return submodular maximization in the bandit setting," Journal of Global Optimization, Springer, vol. 90(3), pages 619-649, November.

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