English(EN) Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement

新的ACE策略增强了在异质图上的GNN训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-27 04:00

研究人员开发了一种名为自适应互补增强（ACE）的新策略，以改进图神经网络（GNN）在异质图上的训练。现有方法在图的简化版本上进行训练，由于简化过程中信息丢失，在异质数据上表现不佳。ACE通过学习重建原始节点特征并纳入考虑局部异质性的正则化来解决这个问题，从而能够更有效地训练复杂的图结构。 AI

影响这项研究可能有助于更有效、可扩展地训练图神经网络，以应用于涉及复杂、异质数据的场景。

排序理由该集群包含一篇详细介绍图神经网络新训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Guoming Li, Jian Yang, Xukun Wang, Zixiao Wang, Shangsong Liang, Yifan Chen · 2026-07-27 04:00

通过自适应互补增强补救异质性下的粗粒度图神经网络训练

arXiv:2607.21885v1 Announce Type: new Abstract: Coarsening-based training for graph neural networks (GNNs), i.e.\ training on coarsened graphs rather than the original large ones, has become a promising direction for scaling GNNs to massive graphs. However, prior work has been ev…