An SEIR model for information propagation with a hot search effect in complex networks

Area of Science:

• Complex networks
• Information propagation dynamics
• Mathematical modeling

Background:

• Understanding information spread is crucial in complex networks.
• Hot searches can significantly influence information diffusion patterns.
• Existing models may not fully capture these effects.

Purpose of the Study:

• To formulate an SEIR model incorporating the impact of hot searches on information propagation.
• To analyze information dynamics in both homogeneous and heterogeneous networks.
• To validate the model using real-world data from Sina Weibo.

Main Methods:

• Developed a Susceptible-Exposed-Infectious-Recovered (SEIR) model.
• Performed mathematical analysis on homogeneous and heterogeneous networks.
• Conducted numerical simulations to assess parameter sensitivity.
• Applied the model to fit empirical data on information spreading.

Main Results:

• Information dynamics are solely determined by the basic propagation number without hot search effects.
• With hot search effects, no information-free equilibrium exists.
• The information-propagating equilibrium is stable if the threshold exceeds 1.
• Model validation confirmed its accuracy in simulating real-world information spread.

Conclusions:

• Hot searches fundamentally alter information propagation dynamics in complex networks.
• The proposed SEIR model accurately captures these altered dynamics.
• The model provides a robust framework for analyzing information spread influenced by trending topics.