English(EN) Hierarchical Graph Representation Learning with Pooling-Induced Substructures

新的GNN方法明确保留子结构以用于层次图任务

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员推出了一种新颖的图池化方法——Pooled-Substructure-Aware GNNs，旨在更好地捕获和处理层次图表示中的子结构。该方法旨在增强图神经网络的表达能力，特别是对于需要超越局部邻域推理的任务。为了便于评估，开发了具有可控层次结构的新合成数据集，以及一个用于评估真实基准中层次模式的指标。与现有的池化基线相比，所提出的方法在具有相关层次结构的任务上表现出优越的性能。 AI

影响增强了图神经网络在复杂层次推理任务中的能力。

排序理由该集群包含一篇详细介绍图表示学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Luca Sbicego, Xiaowen Dong, Dorina Thanou · 2026-11-11 05:00

具有池化诱导子结构的层次图表示学习

arXiv:2611.14126v1 Announce Type: new Abstract: Hierarchical graph-level tasks that require reasoning over substructures that extend beyond local neighborhoods are posing challenges for standard Graph Neural Networks (GNNs). While graph pooling aims to address this by coarsening …

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具有池化诱导子结构的层次图表示学习

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