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Summary
This summary is machine-generated.

This study introduces a new directed hypergraph model to track information spread in social networks, improving accuracy over undirected models. The model better captures real-world propagation dynamics and social relationship strengths.

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Area of Science:

  • Social Network Analysis
  • Information Propagation Modeling
  • Complex Systems

Background:

  • Hypergraphs are used for high-order interactions in social networks.
  • Existing models often assume undirected connections, limiting accuracy in depicting information flow direction and propagation chains.

Purpose of the Study:

  • To develop a social hypernetwork propagation model with directional constraints.
  • To address limitations of undirected models in representing information flow and propagation structures.

Main Methods:

  • Introduced directed hypergraph structure combined with an improved SEIR model.
  • Incorporated social relationship intimacy, directed hypergraph threshold mechanism, and information timeliness attenuation function.

Main Results:

  • Verified the model's effectiveness using the "imposing additional tariffs" event.
  • Analyzed propagation characteristics across different network structures and the impact of user confidence and information timeliness via simulations.

Conclusions:

  • The proposed model accurately characterizes information propagation trends and dynamics in real social networks.
  • Provides a theoretical and methodological basis for predicting and regulating online public opinion.