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A novel game theoretic approach for modeling competitive information diffusion in social networks with heterogeneous nodes

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  • Agha Mohammad Ali Kermani, Mehrdad
  • Fatemi Ardestani, Seyed Farshad
  • Aliahmadi, Alireza
  • Barzinpour, Farnaz

Abstract

Influence maximization deals with identification of the most influential nodes in a social network given an influence model. In this paper, a game theoretic framework is developed that models a competitive influence maximization problem. A novel competitive influence model is additionally proposed that incorporates user heterogeneity, message content, and network structure. The proposed game-theoretic model is solved using Nash Equilibrium in a real-world dataset. It is shown that none of the well-known strategies are stable and at least one player has the incentive to deviate from the proposed strategy. Moreover, violation of Nash equilibrium strategy by each player leads to their reduced payoff. Contrary to previous works, our results demonstrate that graph topology, as well as the nodes’ sociability and initial tendency measures have an effect on the determination of the influential node in the network.

Suggested Citation

  • Agha Mohammad Ali Kermani, Mehrdad & Fatemi Ardestani, Seyed Farshad & Aliahmadi, Alireza & Barzinpour, Farnaz, 2017. "A novel game theoretic approach for modeling competitive information diffusion in social networks with heterogeneous nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 570-582.
    • Handle: RePEc:eee:phsmap:v:466:y:2017:i:c:p:570-582
    DOI: 10.1016/j.physa.2016.09.038
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    References listed on IDEAS

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    Cited by:

    1. Liu, Jiawei & Ding, Jie, 2020. "Requesting for retweeting or donating? A research on how the fundraiser seeks help in the social charitable crowdfunding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    2. Xiao, Yunpeng & Wang, Zheng & Li, Qian & Li, Tun, 2019. "Dynamic model of information diffusion based on multidimensional complex network space and social game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 578-590.
    3. Li, Qian & Song, Chenguang & Wu, Bin & Xiao, Yunpeng & Wang, Bai, 2018. "Social hotspot propagation dynamics model based on heterogeneous mean field and evolutionary games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 324-341.
    4. Hu, Sen & Hu, Bin & Cao, Ya, 2018. "The wider, the better? The interaction between the IoT diffusion and online retailers’ decisions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 196-209.

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