MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection
Score
71首次系统性地用 SQL 统计做元路径选择来加速关系深度学习，角度新颖。
方法描述清晰，实验覆盖多个 GNN 骨干和 RelBench 任务，论证较充分。
直接降低 GNN 在关系数据库上的训练成本，并且与具体 GNN 架构解耦，落地门槛低。
预印本尚未经过完整同行评审，但面向关系深度学习的热点问题，时效性较好。
来源为 arXiv 预印本，方法可复现但可信度取决于后续审稿验证。
Base 79 × T2 Source weight ×0.9 = 71
Curation line 70Selected
Why selected
The method is novel and tested across multiple GNN backbones, but generalization to more heterogeneous database scenarios remains to be proven.
AI commentary
「Training GNNs on relational databases is often slowed by subgraph sampling, and MetaSieve's decision to hand metapath selection to SQL statistics is refreshingly practical. It is worth reading for researchers at the intersection of databases and graph learning, and for algorithm teams working with multi-table data. Because it leaves GNN parameters untouched, it can be dropped into existing pipelines at low cost.」
Quick overview
Relational Deep Learning (RDL) models multi-table databases as graphs and trains graph neural networks, yet subgraph sampling costs are high. MetaSieve leverages SQL join and aggregation capabilities to compute statistics for each candidate metapath extension and prunes uninformative metapaths using a novel scoring function. Since it is independent of GNN parameters, it integrates with various GNN backbones for classification and regression. On the RelBench benchmark, MetaSieve consistently reduces per-epoch training time by large margins while maintaining or often improving accuracy. This makes relational deep learning more practical for real-world multi-table data.
- MetaSieve uses SQL joins and aggregation queries to score metapath usefulness and prunes uninformative paths to reduce subgraph sizes.
- Metapath selection relies only on database statistics and task labels, independent of GNN parameters, easing integration across GNN backbones.
- On RelBench, MetaSieve dramatically reduces per-epoch training time while maintaining or often improving accuracy.
- The method offers an efficient and general acceleration route for GNN training over multi-table relational databases.
Full text
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