English(EN) A Label Noise Robust Algorithm for Graph Data using Neighborhood Label Distribution

新的LENS算法通过鲁棒地处理标签噪声来增强GCN

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了一种名为Leave-one-out Entropy Node Screening (LENS)的新算法，以提高图卷积网络（GCN）在存在标签噪声时的准确性。LENS通过分析移除节点时邻域标签分布熵的变化来识别噪声节点，并包含一种重新标记噪声节点而非丢弃它们的机制。这种方法计算效率高，以比现有方法快得多的速度实现了最先进的节点分类准确性。 AI

影响提高了图神经网络在节点分类等任务中的鲁棒性和效率。

排序理由该集群包含一篇详细介绍图数据新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Pintu Kumar, Nandyala Hemachandra · 2026-10-19 04:00

一种用于图数据的邻域标签分布的标签噪声鲁棒算法

arXiv:2610.22998v1 Announce Type: cross Abstract: Graph Convolutional Networks (GCNs) have demonstrated exceptional performance on graph-structured data. However, their success heavily relies on the availability of clean labels, an assumption that often fails in real-world scenar…

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一种用于图数据的邻域标签分布的标签噪声鲁棒算法

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