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Adaptive seeding for profit maximization in social networks

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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.

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Funding

Funding was provided by National Natural Science Foundation of China (Grant Nos. 61832012, 61771289 and 61672321).

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Authors and Affiliations

  1. School of Computer Science and Technology, Qilu Technology University, **an, China

    Chuangen Gao & Jiguo Yu

  2. Department of Computer Information Systems, Texas A&M University, Central Texas, USA

    Shuyang Gu

  3. School of Mathematics and Information Science, Shaanxi Normal University, **an, China

    Hai Du

  4. Department of Computer Science, University of Texas at Dallas, Dallas, USA

    Weili Wu

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  1. Chuangen Gao
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Correspondence to Jiguo Yu.

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Gao, C., Gu, S., Yu, J. et al. Adaptive seeding for profit maximization in social networks. J Glob Optim 82, 413–432 (2022). https://doi.org/10.1007/s10898-021-01076-1

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