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An interactive propagation model of multiple information in complex networks

Author

Listed:
  • Yao, Yao
  • Li, Yuanyuan
  • Xiong, Xi
  • Wu, Yue
  • Lin, Honggang
  • Ju, Shenggen

Abstract

The relationships between information are intertwined when they are spreading in the network simultaneously. In order to study the impact of different interactions between information on the propagation and find the characteristics of multiple information propagation, we introduce the concept of interaction factor to describe the different relationships between information. Then we model the multiple information propagation based on the Susceptible–Infective–Removed (SIR) model. Based on the proposed model, we set up the dynamic equations by a Markov chain method, and theoretically analyze the propagation threshold. Extensive experiments were conducted on two real datasets and an artificially generated network dataset. The results verify the propagation threshold and show that the different relationships between information have different impact on information propagation.

Suggested Citation

  • Yao, Yao & Li, Yuanyuan & Xiong, Xi & Wu, Yue & Lin, Honggang & Ju, Shenggen, 2020. "An interactive propagation model of multiple information in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315705
    DOI: 10.1016/j.physa.2019.122764
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    References listed on IDEAS

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    1. Xue Zhou & Yong Hu & Yue Wu & Xi Xiong, 2015. "Influence analysis of information erupted on social networks based onSIRmodel," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(02), pages 1-11.
    2. Zhaoyi Li & Fei Xiong & Ximeng Wang & Hongshu Chen & Xi Xiong, 2019. "Topological Influence-Aware Recommendation on Social Networks," Complexity, Hindawi, vol. 2019, pages 1-12, February.
    3. Li, Hui-Jia & Bu, Zhan & Li, Yulong & Zhang, Zhongyuan & Chu, Yanchang & Li, Guijun & Cao, Jie, 2018. "Evolving the attribute flow for dynamical clustering in signed networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 20-27.
    4. Xiao, Yunpeng & Zhang, Li & Li, Qian & Liu, Ling, 2019. "MM-SIS: Model for multiple information spreading in multiplex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 135-146.
    5. Xiong, Xi & Li, Yuanyuan & Qiao, Shaojie & Han, Nan & Wu, Yue & Peng, Jing & Li, Binyong, 2018. "An emotional contagion model for heterogeneous social media with multiple behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 185-202.
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    Cited by:

    1. Zhen Yan & Xiao Zhou & Rong Du, 2024. "An enhanced SIR dynamic model: the timing and changes in public opinion in the process of information diffusion," Electronic Commerce Research, Springer, vol. 24(3), pages 2021-2044, September.
    2. Liu, Li-Ying & Cai, Chao-Ran & Zhang, Si-Ping & Li, Bin-Quan, 2025. "Coexistence of positive and negative information in information-epidemic dynamics on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 666(C).

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