A New Network Feature Affects the Intervention Performance on Public Opinion Dynamic Networks

Area of Science:

• Complex systems
• Network science
• Social dynamics

Background:

• Network structure significantly influences collective opinion in dynamic systems.
• Understanding how network topology affects external interventions is crucial for manipulating group opinions.

Purpose of the Study:

• To investigate the impact of network structure on intervention performance in opinion dynamic models.
• To introduce and validate a new network metric, 'network differential degree' (Ω), for predicting intervention effectiveness.

Main Methods:

• Applied three intervention strategies to the weighted DeGroot opinion dynamic model.
• Defined and calculated the 'network differential degree' (Ω) to quantify the coupling of node degrees and influence.
• Conducted simulations analyzing the correlation between Ω and intervention performance across different intervention scenarios.

Main Results:

• A significant negative correlation was found between intervention performance and network differential degree (Ω) across all tested interventions.
• Smaller Ω values indicate higher intervention success, meaning opinion change is more achievable.
• The network differential degree (Ω) can predict the difficulty of intervening in the weighted DeGroot model.

Conclusions:

• Network differential degree (Ω) is a key factor in determining the efficacy of interventions in opinion dynamic systems.
• This metric provides a predictive tool for assessing intervention feasibility and optimizing strategies.
• Developed a theorem for single-edge addition and an algorithm for optimal edge placement to guide interventions.