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TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks

Author

Listed:
  • He, Qiang
  • Wang, Xingwei
  • Lei, Zhencheng
  • Huang, Min
  • Cai, Yuliang
  • Ma, Lianbo

Abstract

Influence Maximization is an important problem in social networks, and its main goal is to select some most influential initial nodes (i.e., seed nodes) to obtain the maximal influence spread. The existing studies primarily concentrate on the corresponding methods for influence maximization, including greedy algorithms, heuristic algorithms and their extensions to determine the most influential nodes. However, there is little work to ensure efficiency and accuracy of the proposed schemes at the same time. In this paper, a Two-stage Iterative Framework for the Influence Maximization in social networks, (i.e., TIFIM) is proposed. In order to exclude less influential nodes and decrease the computation complexity of TIFIM, in the first stage, an iterative framework in descending order is proposed to select the candidate nodes. In particular, based on the results of the last iteration and the two-hop measure, the First-Last Allocating Strategy (FLAS) is presented to compute the spread benefit of each node. We prove that TIFIM converges to a stable order within the finite iterations. In the second stage, we define the apical dominance to calculate the overlapping phenomenon of spread benefit among nodes and further propose Removal of the Apical Dominance (RAD) to determine seed nodes from the candidate nodes. Moreover, we also prove that the influence spread of TIFIM according to RAD converges to a specific value within finite computations. Finally, simulation results show that the proposed scheme has superior influence spread and running time than other existing ones.

Suggested Citation

  • He, Qiang & Wang, Xingwei & Lei, Zhencheng & Huang, Min & Cai, Yuliang & Ma, Lianbo, 2019. "TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 338-352.
    • Handle: RePEc:eee:apmaco:v:354:y:2019:i:c:p:338-352
    DOI: 10.1016/j.amc.2019.02.056
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    References listed on IDEAS

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    1. repec:sae:jocore:v:55:y:2011:i:6:p:970-955 is not listed on IDEAS
    2. AskariSichani, Omid & Jalili, Mahdi, 2015. "Influence maximization of informed agents in social networks," Applied Mathematics and Computation, Elsevier, vol. 254(C), pages 229-239.
    3. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
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    Cited by:

    1. Gong, Yudong & Liu, Sanyang & Bai, Yiguang, 2021. "A probability-driven structure-aware algorithm for influence maximization under independent cascade model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    2. Mao, Fubing & Ma, Lijia & He, Qiang & Xiao, Gaoxi, 2020. "Match making in complex social networks," Applied Mathematics and Computation, Elsevier, vol. 371(C).
    3. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. Li, Qi & Cheng, Le & Wang, Wei & Li, Xianghua & Li, Shudong & Zhu, Peican, 2023. "Influence maximization through exploring structural information," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    5. Shekhar, Vaibhav & Nayak, Snigdhashree & Mishra, Nachiketa & Mishra, Debasisha, 2021. "Convergence of two-stage iterative scheme for K-weak regular splittings of type II," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    6. Wenguo Yang & Shengminjie Chen & Suixiang Gao & Ruidong Yan, 2020. "Boosting node activity by recommendations in social networks," Journal of Combinatorial Optimization, Springer, vol. 40(3), pages 825-847, October.

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