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Modeling network evolution by multi-agent reinforcement learning
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DOI：10.1038/s41467-026-75382-1.png)
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Abstract
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Modeling network evolution is foundational for understanding and regulating networks. Existing models simplify away the fact that network evolution is often a group decision process, leading to two primary limitations: that nodes lack the ability to learn policy and that there is no coordination among node policies. To address these shortcomings and consider the effectiveness of multi-agent reinforcement learning in solving group decision tasks, this paper proposes a complex Network Evolution model based on Multi-Agent Reinforcement Learning (NEMARL). In our model, swarm intelligence emerging through collaborative interactions among autonomous nodes drives the evolution of the network structure. Our extensive experiments demonstrate that the NEMARL model accurately reproduces classical network characteristics and fits real network data well. Furthermore, we demonstrate its effectiveness through scenario testing. This paper introduces NEMARL, a multi-agent reinforcement learning framework for network evolution that models node-level decision making and coordination. The approach reproduces key network properties, fits real-world data, and demonstrates effectiveness across multiple scenario tests.
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