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Modeling network evolution by multi-agent reinforcement learning
Dong Li1,2,3, Tianwei Lin1, Zhaoyang Bao1
1School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China.
Nature Communications
|July 21, 2026
Summary
This study introduces a novel Network Evolution model based on Multi-Agent Reinforcement Learning (NEMARL) to capture group decision-making in network evolution. NEMARL effectively models complex network structures and real-world data, highlighting swarm intelligence in network dynamics.
Area of Science:
- Complex Systems
- Network Science
- Artificial Intelligence
Background:
- Network evolution modeling is crucial but often overlooks group decision-making processes.
- Existing models lack node policy learning and inter-node policy coordination.
- These limitations hinder accurate representation of real-world network dynamics.
Purpose of the Study:
- To propose a novel model for network evolution that incorporates group decision-making.
- To address the limitations of existing models by enabling policy learning and coordination among nodes.
- To evaluate the effectiveness of multi-agent reinforcement learning in complex network evolution.
Main Methods:
- Developed a complex Network Evolution model based on Multi-Agent Reinforcement Learning (NEMARL).
- Utilized swarm intelligence emerging from collaborative interactions among autonomous nodes.
- Employed extensive experiments and scenario testing for validation.
Main Results:
- The NEMARL model accurately reproduces classical network characteristics.
- The model demonstrates a strong fit with real-world network data.
- Effectiveness was validated through various scenario tests.
Conclusions:
- NEMARL successfully models complex network evolution driven by group decisions.
- The model offers a more realistic approach compared to existing methods.
- This framework provides a powerful tool for understanding and regulating evolving networks.
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