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English(EN) Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size

新的分数热核在训练数据最少的情况下改进了图学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的分数热核框架，用于半监督图学习，旨在即使在训练数据有限的情况下也能提高性能。该方法结合了非局部传播和持续的标签信息，防止了常见的过平滑问题。分数阶和扩散时间是控制标签传播和谱权重 Thus, the fractional order and diffusion time are key parameters that control label propagation and spectral weighting, leading to significant accuracy improvements on datasets like Two-Moon, Cora, and CiteSeer, particularly when using only one training label per class. AI

影响 这项研究为改进基于图的机器学习模型提供了一种新技术，特别是在标记数据稀缺的情况下，有可能提高推荐系统和社交网络分析等领域的性能。

排序理由 详细介绍图学习新方法的学术论文。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新的分数热核在训练数据最少的情况下改进了图学习

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详细介绍图学习新方法的学术论文。 [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Farid Bozorgnia, Vyacheslav Kungurtsev, Shirali Kadyrov, Mohsen Yousefnezhad ·

    用于小训练样本量的半监督图学习的分数热核

    arXiv:2510.04440v2 Announce Type: replace Abstract: We develop a source-driven fractional heat-kernel framework for semi-super\-vised graph learning that combines nonlocal propagation with sustained label information. A fixed nonzero label source compatible with the Laplacian nul…

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