English(EN) Towards Training and Inference of Graph Neural Networks in Graph Databases

图神经网络与图数据库集成，实现更快的训练和推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员探索了图神经网络（GNN）与图数据库的集成，这是一种处理大型图数据的新方法。该研究表明，图数据库可以直接执行GNN训练和推理任务，包括采样和特征检索。优化显示，训练速度显著加快，大约快2.5倍，同时不影响准确性。此外，数据库内推理比将图加载到RAM的传统方法快10倍以上，且没有额外的内存开销。 AI

影响这项研究通过利用现有的图数据库基础设施，可以显著提高GNN在大规模数据集上操作的效率和可扩展性。

排序理由该集群包含一篇详细介绍GNN与图数据库集成的新颖技术方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Victor Pekkari, Harry Wik, Alfred Clemedtson, Borun Shi, Xuan-Son Vu · 2026-10-15 04:00

面向图数据库中图神经网络的训练与推理

arXiv:2610.19142v1 Announce Type: new Abstract: Graph Neural Networks on large graphs commonly rely on in-memory graph storage with ad hoc data processing pipelines for both training and inference. Graph databases provide scalable storage and efficient traversal for graphs that c…

本文如何被排名

报道来源 [1]

面向图数据库中图神经网络的训练与推理

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