ArticleNature communications2026
Modeling network evolution by multi-agent reinforcement learning.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
9 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.