Guiding propagation to localized target nodes in complex networks.

Area of Science:

• Complex Networks
• Network Dynamics
• Computational Science

Background:

• Spreading dynamics are crucial for modeling real-world processes like epidemic contagion and information propagation.
• Existing methods focus on vital node identification and immunization for controlling spread.
• Target spreading is an emerging research area focused on controlling propagation towards specific node groups.

Purpose of the Study:

• To develop adaptive models for guiding random initial spreading towards localized target nodes in a network.
• To investigate the influence of infection and recovery rates on the proposed guided spreading models.
• To explore the feasibility of achieving adaptive target spreading with multiple target node groups.

Main Methods:

• Proposed guided propagation and reversed guided propagation models.
• Adaptive allocation of recovery nodes in each spreading step.
• Detailed study of infection and recovery rate impacts on model performance.

Main Results:

• Simulation results validate the proposed models in various scenarios.
• Demonstrated the effectiveness of adaptive strategies in controlling spreading dynamics.
• Confirmed the possibility of achieving target spreading even with multiple target groups.

Conclusions:

• Adaptive target spreading models offer a novel approach to control network propagation.
• The models successfully guide spreading towards predefined targets.
• The approach is viable for complex networks with multiple target objectives.