English(EN) Label Ensemble Perspective for Reduced-Complexity Graph Node Classification under Label Noise

新的LEGNN方法解决了图节点分类中的标签噪声问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员推出了一种新颖的方法——标签集成图神经网络（LEGNN），旨在提高图节点分类在面对标签噪声时的鲁棒性。与现有专注于识别可靠标签的方法不同，LEGNN将问题视为一个标签集成任务，汇集多个信息性标签以减轻不准确性的影响。该方法包括一个两步过程：引导邻近上下文以创建多样化的标签集，然后采用部分标签学习策略进行训练。LEGNN在六个数据集上展示了强大的性能和计算效率，并显示出对大规模图的良好可扩展性。 AI

影响为图节点分类任务提供了一种更高效、更鲁棒的方法，有可能在带噪声数据应用中提高性能。

排序理由详细介绍图节点分类新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Rui Zhao, Bin Shi, Zhiming Liang, Jianfei Ruan, Kai Sun, Bo Dong, Lu Lin · 2026-10-28 04:00

用于标签噪声下低复杂度图节点分类的标签集成视角

arXiv:2411.11020v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by \emph{label noise}, that is, a small amount of incorre…

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用于标签噪声下低复杂度图节点分类的标签集成视角

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