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

arXiv: https://arxiv.org/abs/2506.17925 · HTML: https://mlsanity.com/paper/2506.17925/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 79 references that are in the map.
- First posted on arXiv 22 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 10 of 10)

- [Networks beyond pairwise interactions: structure and dynamics](https://mlsanity.com/paper/2006.01764/) (2020)
- [Statistical physics of human cooperation](https://mlsanity.com/paper/1705.07161/) (2017)
- [Route Planning in Transportation Networks](https://mlsanity.com/paper/1504.05140/) (2015)
- [Artificial Intelligence Development Races in Heterogeneous Settings](https://mlsanity.com/paper/2012.15234/) (2020)
- [LocalDrop: A Hybrid Regularization for Deep Neural Networks](https://mlsanity.com/paper/2103.00719/) (2021)
- [Social Diversity Reduces the Complexity and Cost of Fostering Fairness](https://mlsanity.com/paper/2211.10517/) (2022)
- [Evolutionary games on multilayer networks: A colloquium](https://mlsanity.com/paper/1504.04359/) (2015)
- [The evolution of trust and trustworthiness](https://mlsanity.com/paper/2008.12948/) (2020)
- [Social learning with complex contagion](https://mlsanity.com/paper/2406.14922/) (2024)
- [An Evolutionary Game With the Game Transitions Based on the Markov Process](https://mlsanity.com/paper/2502.05742/) (2025)

## Built on by (most cited first, 1 of 1)

- [An Evolutionary Computation Framework for Multi-Agent Q-Learning with Mean-Field Environmental Feedback](https://mlsanity.com/paper/2609.13253/) (2026)

## Abstract (arXiv)

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.