English(EN) MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection

MetaSieve 通过基于 SQL 的元路径选择加速关系深度学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员开发了 MetaSieve，这是一个新颖的层，旨在通过优化子图选择来加速关系深度学习 (RDL)。MetaSieve 利用 SQL 查询来分析数据库统计信息，并识别用于图神经网络训练的信息性元路径，修剪不太有用的元路径以降低计算成本。在 RelBench 基准上的评估表明，MetaSieve 在显著缩短训练时间的同时，通常还能提高模型准确性。 AI

影响该方法可以显著减少在关系数据库上训练图神经网络所需的计算资源。

排序理由该集群描述了一篇详细介绍改进机器学习性能的新颖方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Fahim Shahriar Khan, Ashraf Aboulnaga · 2026-08-27 04:00

MetaSieve：通过基于SQL的元路径选择实现更快的关系深度学习

arXiv:2608.25903v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) is an effective approach to machine learning over multi-table relational databases. In RDL, a database is modeled as a graph in which each row is a node and each foreign-key relation is an edge, and …

本文如何被排名

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MetaSieve：通过基于SQL的元路径选择实现更快的关系深度学习

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