A sequential seed scheduling heuristic based on determinate and latent margin for influence maximization problem with limited budget

Jianxin Tang, Fuqing Zhao, Ruisheng Zhang, Baoqiang Chai and Shilu Di
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Jianxin Tang: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu 730050, P. R. China
Fuqing Zhao: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu 730050, P. R. China
Ruisheng Zhang: #x2020;School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China
Baoqiang Chai: #x2020;School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China
Shilu Di: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu 730050, P. R. China

International Journal of Modern Physics C (IJMPC), 2021, vol. 32, issue 06, 1-18

Abstract: The influence maximization problem in social networks aims to select a subset of most influential nodes, denoted as seed set, to maximize the influence diffusion of the seed nodes. The majority of existing works on this problem would ignite all the seed nodes simultaneously at the beginning of the diffusion process and let the influence diffuses passively in the network. However, it cannot depict the practical dynamics exactly of viral marketing campaigns in reality and fails to provide driving policies to control over the diffusion. In this paper, we focus on the dynamic influence maximization problem with limited budget to study the scheduling strategies including which influential node is to be seeded during the diffusion process and when to seed it at the right time. A time-dependent seed activating feedback scheme is modeled firstly by considering the time factor and its impact on the influence obligation in diffusion process. Then a scheduling heuristic based on determinate and latent margin is proposed to evaluate the marginal return of candidate nodes and activate the right seed node to promote the viral marketing. Extensive experiments on four social networks show that the proposed algorithm achieves significantly better results than a typical static influence maximization algorithm based on swarm intelligence and can improve the influence propagation under the time-dependent diffusion model comparing with the centrality-based scheduling heuristics.

Keywords: Social networks; scheduling influence maximization; seed activating strategy; latent effect; viral marketing (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0129183121500790

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