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This study introduces a new hypernetwork information dissemination model (SSEIR) for social networks. It accurately simulates complex interactions and improves understanding of online information spread.

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Area of Science:

  • Computational Social Science
  • Network Science
  • Information Theory

Background:

  • Social networks present unique challenges for information dissemination modeling due to complex user interactions.
  • Traditional models like SEIR may not fully capture the high-order, multi-user dynamics inherent in online platforms.

Purpose of the Study:

  • To develop a novel hypernetwork information dissemination model (SSEIR) for online social networks.
  • To enhance the traditional SEIR model by incorporating hypergraph theory and refining state transitions.
  • To accurately simulate and analyze information propagation in complex online social structures.

Main Methods:

  • Augmented the traditional SEIR model using hypergraph theory to create the SSEIR model.
  • Transformed the susceptible state into active (Sa) and inactive (Si) states.
  • Incorporated reaction process (RP) strategies and derived differential dynamical equations.
  • Utilized mean field theory for theoretical derivation and simulation experiments on various network structures and real datasets.

Main Results:

  • The SSEIR model effectively simulates information dissemination and diffusion in online social networks.
  • Demonstrated superior data fitting capabilities compared to traditional models.
  • Successfully illustrated the internal mechanisms and dynamic evolutionary patterns of information spread within hypernetwork structures.
  • Validated the model's practicality through application on real-world network data.

Conclusions:

  • The SSEIR model provides a more accurate representation of information dissemination in complex online social networks.
  • This study enriches the theoretical framework of information propagation.
  • Offers a scientific foundation for practical applications like news dissemination, public opinion management, and rumor monitoring.