Finding key players in complex networks through deep reinforcement learning.

Area of Science:

• Network Science
• Complex Systems
• Artificial Intelligence

Background:

• Identifying key players is crucial for network functionality, with applications in immunization, epidemic control, and marketing.
• Existing methods for finding key players are often NP-hard, lacking efficient and unified solutions.
• Approximate and heuristic strategies exist but are specific to application scenarios.

Purpose of the Study:

• To introduce a unified and efficient deep reinforcement learning framework, FINDER, for solving key player problems.
• To enable the application of a single framework across diverse influencer finding problems.
• To leverage deep learning for understanding complex network organization.

Main Methods:

• Developed FINDER, a deep reinforcement learning framework.
• Trained FINDER on small synthetic networks from toy models.
• Applied the trained framework to various influencer finding problems.

Main Results:

• FINDER significantly outperforms existing methods in solution quality across diverse settings.
• FINDER is orders of magnitude faster than existing methods for large networks.
• Demonstrated the framework's versatility in addressing a wide spectrum of problems.

Conclusions:

• FINDER provides a novel and efficient approach to key player identification in complex networks.
• Deep reinforcement learning offers a powerful tool for network science and understanding network organizing principles.
• The framework facilitates the design of more robust networks against attacks and failures.