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PaSta framework tackles noisy node classification with partial label learning

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced PaSta, a novel framework designed to address the challenges of noisy node classification in graph-based machine learning. Unlike traditional methods that rely on one-hot labels, PaSta utilizes partial label learning to improve robustness against corrupted or unreliable node labels. The framework trains multiple annotators to generate high-quality partial labels and employs a self-training strategy to iteratively refine these labels and optimize the classification model. Experiments show PaSta achieves an average improvement of 1.1% in classification performance across various noise levels. AI

IMPACT Improves robustness in graph-based machine learning tasks with noisy data.

RANK_REASON The cluster describes a research paper published on arXiv detailing a new method for noisy node classification.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

PaSta framework tackles noisy node classification with partial label learning

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The cluster describes a research paper published on arXiv detailing a new method for noisy node classification.
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COVERAGE [2]

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

    PaSta: Noisy Node Classification with Partial Label Learning

    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: Noisy Node Classification with Partial Label Learning

    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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