English(EN) Toward Maximizing Algebraic Connectivity via Density-Dependent Backbone Construction and Reinforcement Learning Completion

新方法利用强化学习最大化图连通性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了一种新颖的图拓扑构建方法，旨在最大化代数连通性。该方法包括两个阶段：首先，使用密度依赖策略构建结构骨架，然后，使用强化学习优化剩余连接。与现有技术相比，该方法表现出优越的性能和可扩展性，为高效网络设计提出了一种家族优先的结构策略。 AI

影响这项研究可能在电信和分布式计算等各个领域带来更高效、可扩展的网络设计。

排序理由该条目是一篇学术论文，详细介绍了一种新的图拓扑构建方法。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Newsworthiness bucket: Tool; 该条目是一篇学术论文，详细介绍了一种新的图拓扑构建方法。[lever_c_demoted from research: ic=1 ai=0.7]
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arXiv cs.LG TIER_1 English(EN) · Zeru Zhu, Marco Gamarra, Ji Liu · 2026-10-12 04:00

通过密度依赖骨架构建和强化学习补全实现代数连通性最大化

arXiv:2610.12697v1 Announce Type: cross Abstract: This paper studies an open network design problem of constructing graph topologies with maximum algebraic connectivity under fixed numbers of vertices and edges. A density-dependent approach is proposed that decomposes the design …