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English(EN) PaSta: Noisy Node Classification with Partial Label Learning

PaSta框架通过部分标签学习解决噪声节点分类问题

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出PaSta，一个旨在解决图机器学习中噪声节点分类挑战的新型框架。与依赖独热编码标签的传统方法不同，PaSta利用部分标签学习来提高对损坏或不可靠节点标签的鲁棒性。该框架训练多个标注器生成高质量的部分标签，并采用自训练策略来迭代地优化这些标签和分类模型。实验表明，PaSta在各种噪声水平下，分类性能平均提高了1.1%。 AI

影响 提高了具有噪声数据的图机器学习任务的鲁棒性。

排序理由 该集群描述了一篇在arXiv上发表的研究论文，详细介绍了一种新的噪声节点分类方法。

在 Hugging Face Daily Papers 阅读 →

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PaSta框架通过部分标签学习解决噪声节点分类问题

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该集群描述了一篇在arXiv上发表的研究论文，详细介绍了一种新的噪声节点分类方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yujing Liu, Yixin Liu, Yu Zheng, Yue Tan, Alan Wee-Chung Liew, Shirui Pan ·

    PaSta：基于部分标签学习的噪声节点分类

    arXiv:2608.25365v1 Announce Type: new Abstract: Noisy node classification problem is a fundamental yet challenging task for real-world graph-related web services, where node labels are often corrupted or unreliable due to weak supervision or automatic annotation. However, existin…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    PaSta：利用部分标签学习进行噪声节点分类

    Noisy node classification problem is a fundamental yet challenging task for real-world graph-related web services, where node labels are often corrupted or unreliable due to weak supervision or automatic annotation. However, existing methods typically train models based on one-ho…

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