Information dissemination evolution under group feedback

Area of Science:

• Network Science
• Information Dissemination
• Computational Social Science

Background:

• Understanding information spread in networked groups is crucial.
• Existing models often overlook group dynamics and feedback mechanisms.
• Group behavior significantly influences information diffusion patterns.

Purpose of the Study:

• To investigate the evolutionary characteristics of group networks during information dissemination.
• To develop and validate a feedback-based model for information spread in group networks.
• To analyze the impact of group attributes on network evolution.

Main Methods:

• Dividing networks into groups with dynamic activity and information response.
• Developing a feedback model accounting for intra-group and inter-group information flow.
• Simulating the model on scale-free and small-world networks.
• Validating the model on a real-world network.

Main Results:

• Group positivity emerged as the primary driver of evolutionary outcomes in small-world networks, irrespective of initial group size.
• In scale-free networks, group positivity was the key factor influencing average group degree, independent of initial average degree.
• The model demonstrated effectiveness and rationality when applied to a real-world network.

Conclusions:

• Feedback mechanisms and group positivity are critical determinants of information dissemination and network evolution.
• Network structure (scale-free vs. small-world) interacts with group dynamics to shape information spread.
• The developed model provides a robust framework for studying information diffusion in complex group networks.