Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure

2025-06-22

AI Summary

Existing studies struggle to characterize the co-evolutionary mechanism between individual birth–death dynamics and community structure in dynamic systems. Method: We propose a two-dimensional spatial network evolution model integrating individual lifecycles, evolutionary game theory, and reinforcement learning—specifically Q-learning—to enable agents to update strategies based on local interactions. Crucially, we embed stochastic birth–death mechanisms directly into network topology evolution. Contribution/Results: Through systematic multi-agent simulations and validation on real-world networks, we uncover how learning rate, payoff matrix, and spatial dimension jointly govern the emergence of cooperation, as well as the speed, scale, and stability of community formation. The model successfully reproduces the spontaneous expansion of cooperative behavior and the concurrent emergence of hierarchical community structures. This provides a novel paradigm for understanding the self-organized genesis of social structure in complex adaptive systems.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Machine Learning: Evolutionary Learning

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Applications and emergent phenomena

Abstract

Complex networks serve as abstract models for understanding real-world complex systems and provide frameworks for studying structured dynamical systems. This article addresses limitations in current studies on the exploration of individual birth-death and the development of community structures within dynamic systems. To bridge this gap, we propose a networked evolution model that includes the birth and death of individuals, incorporating reinforcement learning through games among individuals. Each individual has a lifespan following an arbitrary distribution, engages in games with network neighbors, selects actions using Q-learning in reinforcement learning, and moves within a two-dimensional space. The developed theories are validated through extensive experiments. Besides, we observe the evolution of cooperative behaviors and community structures in systems both with and without the birth-death process. The fitting of real-world populations and networks demonstrates the practicality of our model. Furthermore, comprehensive analyses of the model reveal that exploitation rates and payoff parameters determine the emergence of communities, learning rates affect the speed of community formation, discount factors influence stability, and two-dimensional space dimensions dictate community size. Our model offers a novel perspective on real-world community development and provides a valuable framework for studying population dynamics behaviors.

Problem

• Modeling dynamic evolution of complex networks with reinforcement learning
• Studying individual birth-death and community structure development
• Analyzing impact of learning rates and payoff parameters on communities

Innovation

• Reinforcement learning with Q-learning for individual actions
• Dynamic birth-death process in network evolution
• Two-dimensional spatial movement for community formation