English(EN) SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network

SHAKE-GNN：可扩展分层图神经网络框架发布

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员推出了一种用于图级任务的新框架SHAKE-GNN，旨在提高可扩展性。这种新颖的方法利用Kirchhoff Forest的分层结构来生成图的多尺度表示。SHAKE-GNN在效率和性能之间提供了灵活的权衡，并采用改进的数据驱动策略进行参数选择。在大规模图分类基准测试上的实验表明，SHAKE-GNN在展示出增强的可扩展性的同时，取得了具有竞争力的结果。 AI

影响这项研究可以实现对大型图进行更有效的处理，以用于各种机器学习任务。

排序理由该集群包含一篇学术论文，详细介绍了图神经网络的新模型架构。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Zhipu Cui, Johannes Lutzeyer · 2026-09-01 04:00

SHAKE-GNN: 可扩展分层Kirchhoff-Forest图神经网络

arXiv:2509.22100v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have achieved remarkable success across a range of learning tasks. However, scaling GNNs to large graphs remains a significant challenge, especially for graph-level tasks. In this work, we introduce …

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SHAKE-GNN: 可扩展分层Kirchhoff-Forest图神经网络

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