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English(EN) S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs

新的S2Aligner框架增强了稀疏数据上的图-文本预训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了S2Aligner，一个旨在改进文本属性图预训练的新框架，特别是那些文本信息稀疏的图。该方法将语义对齐与结构建模分离，利用拓扑感知信号来增强对齐而不破坏语义空间。S2Aligner还结合了稀疏感知跨域风险平衡，以更好地处理不同域中嘈杂或不均匀的文本数据，从而提高下游任务的泛化能力。 AI

影响 通过改进稀疏文本属性图上的预训练来增强图基础模型，有可能提升下游应用的性能。

排序理由 该集群包含一篇详细介绍图-文本预训练新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的S2Aligner框架增强了稀疏数据上的图-文本预训练

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该集群包含一篇详细介绍图-文本预训练新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ruijie Wang ·

    S2Aligner：稀疏文本属性图的配对高效且可迁移的预训练

    Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation models, where LLM-as-Aligner methods align graph and text representations through the semantic knowledge of large language models. However, these methods usually assume that node te…

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