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Influence maximization in social networks under an independent cascade-based model

Author

Listed:
  • Wang, Qiyao
  • Jin, Yuehui
  • Lin, Zhen
  • Cheng, Shiduan
  • Yang, Tan

Abstract

The rapid growth of online social networks is important for viral marketing. Influence maximization refers to the process of finding influential users who make the most of information or product adoption. An independent cascade-based model for influence maximization, called IMIC-OC, was proposed to calculate positive influence. We assumed that influential users spread positive opinions. At the beginning, users held positive or negative opinions as their initial opinions. When more users became involved in the discussions, users balanced their own opinions and those of their neighbors. The number of users who did not change positive opinions was used to determine positive influence. Corresponding influential users who had maximum positive influence were then obtained. Experiments were conducted on three real networks, namely, Facebook, HEP-PH and Epinions, to calculate maximum positive influence based on the IMIC-OC model and two other baseline methods. The proposed model resulted in larger positive influence, thus indicating better performance compared with the baseline methods.

Suggested Citation

  • Wang, Qiyao & Jin, Yuehui & Lin, Zhen & Cheng, Shiduan & Yang, Tan, 2016. "Influence maximization in social networks under an independent cascade-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 20-34.
    • Handle: RePEc:eee:phsmap:v:444:y:2016:i:c:p:20-34
    DOI: 10.1016/j.physa.2015.10.020
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    References listed on IDEAS

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    1. Heidari, Mehdi & Asadpour, Masoud & Faili, Hesham, 2015. "SMG: Fast scalable greedy algorithm for influence maximization in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 124-133.
    2. Si, Xia-Meng & Liu, Yun & Xiong, Fei & Zhang, Yan-Chao & Ding, Fei & Cheng, Hui, 2010. "Effects of selective attention on continuous opinions and discrete decisions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3711-3719.
    3. Wu, Yanlei & Yang, Yang & Jiang, Fei & Jin, Shuyuan & Xu, Jin, 2014. "Coritivity-based influence maximization in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 467-480.
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    Cited by:

    1. Wang, Qiyao & Jin, Yuehui & Cheng, Shiduan & Yang, Tan, 2017. "ConformRank: A conformity-based rank for finding top-k influential users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 39-48.
    2. Fei Wang & Zhenfang Zhu & Peiyu Liu & Peipei Wang, 2019. "Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect," Future Internet, MDPI, vol. 11(4), pages 1-16, April.

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