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

图数据库赋能更快的GNN训练与推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-13 20:09

研究人员探索了使用图数据库进行图神经网络（GNN）的训练与推理，超越了传统的内存存储。他们的工作表明，图数据库可以使用Cypher等查询语言直接执行GNN的采样、特征检索甚至完整的正向传播。这种方法提供了显著的性能提升，训练速度约提高2.5倍，推理速度比传统方法快10倍以上，且没有额外的内存开销。 AI

影响通过利用现有的图数据库基础设施，实现了更高效的图神经网络训练与推理。

排序理由学术论文，详细介绍了GNN训练与推理的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-13 20:09

面向图数据库中图神经网络的训练与推理

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 can evolve over time, but their applicability in …

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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