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English(EN) Learning the Graphical Nature of Symmetries

新数据集和图神经网络推动有限群对称性研究

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个包含超过131,000个Cayley图的新数据集，作为研究有限群属性如何在图可观测量中体现的基准。这项工作还为OEIS贡献了新的枚举序列，并识别出经验规律，从而提出可检验的关于图属性的猜想。对经典模型、MLP和图神经网络（GNNs）的比较表明，GNNs，特别是GIN和GCN，能够直接从图数据中有效预测代数群属性。 AI

影响 推动了GNNs在从图结构预测代数属性方面的应用，可能影响依赖于对称性分析的领域。

排序理由 该集群包含一篇学术论文，详细介绍了使用机器学习模型研究数学属性的新数据集和基准。

在 arXiv stat.ML 阅读 →

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新数据集和图神经网络推动有限群对称性研究

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该集群包含一篇学术论文，详细介绍了使用机器学习模型研究数学属性的新数据集和基准。
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Rashid Barket, Enrico Grimaldi, Yacoub Hendi, Edward Hirst, Adam Onus, Harmeet Singh ·

    学习对称性的图解性质

    arXiv:2607.12026v1 Announce Type: new Abstract: Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured, compared, and learned. In this paper, a dataset of $131{,}406$ Cayley graphs is c…

  2. arXiv stat.ML TIER_1 English(EN) · Harmeet Singh ·

    学习对称性的图解性质

    Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured, compared, and learned. In this paper, a dataset of $131{,}406$ Cayley graphs is constructed, covering all groups of order at most…

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