English(EN) EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks

新的EDiS框架优化图神经网络训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了EDiS，一个旨在优化图神经网络（GNN）训练的框架，通过解决边选择相关的计算成本问题。EDiS将初始结构提取与每轮周期的图组合分开，允许缓存的边不相交子图的重用。该方法能够在训练周期中动态组合图，而无需重复采样或重新计算，从而在各种节点分类基准测试中提高了性能。 AI

影响优化了GNN训练效率，可能降低计算成本并提高基于图的机器学习任务的性能。

排序理由这是一篇详细介绍图神经网络新框架的研究论文。

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arXiv cs.LG TIER_1 English(EN) · Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt, S M Ferdous, Ryan A. Rossi, Nesreen K. Ahmed, Baris Coskunuzer, Alex Pothen, Lakshman Tamil, Mahantesh M Halappanavar · 2026-10-08 04:00

EDiS：面向图神经网络的边不相交子图稀疏化框架

arXiv:2610.09059v1 Announce Type: new Abstract: Sparse GNN training reduces computation, but deciding which edges to keep can be costly. Reusing one sparse graph is cheap, but locks training to a fixed topology, while varying it across epochs can require repeated sampling or reco…