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Time-bounded targeted influence spread in online social networks

  • 1209: Recent Advances on Social Media Analytics and Multimedia Systems: Issues and Challenges
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Abstract

Influence maximization with application to viral marketing aims to find a small set of influencers in a social network to maximize the number of influenced users under a certain propagation model. However, in many actual marketing scenarios, companies are usually concerned about precision marketing before the specified deadline. In this paper, different from most of influence maximization problems, we focus on an issue of time-bounded targeted influence spread, where it asks for finding a seed set to maximize the influence on a specific set of target users within a bounded time in the network. This problem is NP-hard, and its objective function maintains the monotonicity and submodularity. We devise a greedy algorithm with approximate guarantee to effectively solve the problem. To overcome the low calculational efficiency of this algorithm in large networks, we further propose several efficient heuristic algorithms to greatly speed up the seed selection. Extensive experiments over real-world available social networks of different sizes show the effectiveness and efficiency of the proposed algorithms.

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Notes

  1. NP-hard problem refers to a problem that all NP problems can be reduced to within polynomial time complexity, while NP problem is a problem that can verify a solution in polynomial time. In general, #P-hard problem is more complicated than NP-hard problem. Therefore, in practice, it usually needs to find the approximate solutions for such problems.

  2. The symbol “\(\setminus\)” represents the difference set in the set operation.

  3. The proposed algorithms are based on the greedy algorithm in Algorithm 1, and their difference is the method of calculating the incremental influence spread.

  4. We do not compare the greedy algorithm using Monte Carlo simulation. It mainly considers that the number of possible random graphs is exponential and usually very large, and a sufficient number of random simulations are required to obtain the accurate estimates with high probability. As a result, the time consumption of this method is too high for all social network datasets.

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Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Lei Yu, Guohui Li & Ling Yuan

  2. Wuhan Fiberhome Technical Service Co., Ltd, Wuhan, China

    Li Zhang

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  1. Lei Yu
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  2. Guohui Li
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  3. Ling Yuan
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Correspondence to Ling Yuan.

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Yu, L., Li, G., Yuan, L. et al. Time-bounded targeted influence spread in online social networks. Multimed Tools Appl 82, 9065–9081 (2023). https://doi.org/10.1007/s11042-021-11461-3

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  • DOI: https://doi.org/10.1007/s11042-021-11461-3

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