Mitigating cascades in coevolving networks with targeted rewiring.

Area of Science:

• Network science
• Sociophysics
• Complex systems

Background:

• Understanding how network structure influences opinion dynamics is crucial.
• Previous models often lack efficiency in managing adoption cascades and network adaptation.

Purpose of the Study:

• To investigate the coevolution of network structure and opinion dynamics.
• To introduce a novel targeted rewiring mechanism involving superspreader nodes.

Main Methods:

• Integration of a threshold-based complex contagion model with targeted rewiring.
• Mean-field theoretical analysis.
• Numerical simulations.

Main Results:

• Targeted rewiring by superspreader nodes efficiently contains adoption cascades.
• Significantly reduces the number of rewiring operations required.
• Localizes structural adaptation to critical network points, ensuring economical and stable evolution.

Conclusions:

• The superspreader mechanism offers a more efficient and realistic approach to network intervention.
• Substantially reduces intervention costs and rewiring burden with minimal structural change.