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English(EN) OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation

新研究应对文本属性图学习挑战

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文介绍了用于提高文本属性图（TAGs）学习能力的方法和基准，TAGs将关系结构与文本数据相结合。第一篇论文《半监督文本属性图蒸馏》提出了一个名为\algo{}的框架，该框架使用Wasserstein距离来增强可扩展性，并集成大型语言模型（LLMs）以在半监督设置中获得更好的性能。第二篇论文《OpenRTAG》提出了一个综合基准，旨在评估图神经网络（GNNs）和LLM-GNNs在处理存在各种质量退化的不完美、真实世界TAG数据时的鲁棒性。 AI

影响 这些进展可以提高依赖于理解数据内复杂关系（特别是集成文本信息的数据）的AI系统的性能和鲁棒性。

排序理由 两篇在arXiv上发表的学术论文，提出了新的图学习方法和基准。

在 arXiv cs.AI 阅读 →

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新研究应对文本属性图学习挑战

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两篇在arXiv上发表的学术论文，提出了新的图学习方法和基准。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Yurui Lai, Samir Moustafa, Renchi Yang, Tsz Nam Chan ·

    半监督文本属性图蒸馏

    arXiv:2607.20477v1 Announce Type: new Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics. Existing representation learning methods over TAGs suffer from severe scalability bottlenecks, …

  2. arXiv cs.AI TIER_1 English(EN) · Yuze Dai, Zhihan Zhang, Yan Zhao, Ruoyu Wu, Xunkai Li, Zekai Chen, Qiangqiang Dai, Hongchao Qin, Ronghua Li ·

    OpenRTAG：数据质量下降下鲁棒文本归因图学习的综合基准

    arXiv:2607.19108v1 Announce Type: new Abstract: Text-attributed graphs (TAGs) are an important graph data form that combine relational structure with rich node text. However, real-world TAGs are often imperfect, with quality issues arising from text, structure, and labels, and ty…

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

    OpenRTAG：数据质量下降下鲁棒文本归因图学习的综合基准

    Text-attributed graphs (TAGs) are an important graph data form that combine relational structure with rich node text. However, real-world TAGs are often imperfect, with quality issues arising from text, structure, and labels, and typically manifesting as sparsity, noise, and imba…

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