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English(EN) Graph Label Alignment: A Diagnostic Atlas for Graph Classification

新的诊断工具揭示图分类基准的挑战

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了一种名为图标签对齐的新诊断工具，以更好地理解图分类基准的难度。该方法使用结构标签对齐（SLA）和特征标签对齐（FLA）两个探针，来衡量标签从数据的边际视图中可访问的难易程度。结果表明，标签对齐度低的的数据集对标准的图神经网络和图变换器来说尤其具有挑战性，这表明在这些情况下取得成功依赖于保留超越简单池化表示的结构-特征对应关系。 AI

影响 提供了一种评估和理解图分类任务难度的新方法，可能指导图神经网络的未来研究。

排序理由 学术论文，介绍了一种用于图分类基准的新诊断方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的诊断工具揭示图分类基准的挑战

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学术论文，介绍了一种用于图分类基准的新诊断方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Neelam Akula, Varun Shiralkar, Murat Kantarcioglu, Baris Coskunuzer ·

    Graph Label Alignment: A Diagnostic Atlas for Graph Classification

    arXiv:2611.14760v1 Announce Type: cross Abstract: Graph classification benchmarks vary widely in difficulty: some are solved by simple pooled baselines, while others remain challenging for GNNs and graph transformers. We take a data-centric view and ask whether benchmark success …

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