MetaSieve is a metapath selection layer that reduces subgraph size in relational deep learning by pruning uninformative metapaths using SQL join and aggregation statistics. It scores candidate metapath extensions with a lightweight function that favors informative yet lightweight paths, discarding those below a threshold. The method is independent of GNN parameters and, when applied to the RelBench benchmark, consistently cuts per‑epoch training time while preserving or improving accuracy.
By Fahim Shahriar Khan, Ashraf Aboulnaga
arXiv:2607. 03659v1 Announce Type: cross Abstract: Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult.
By Kazi F. Akhter, Bharath Ajendla, Manar D. Samad
arXiv:2608. 13023v1 Announce Type: new Abstract: Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning.
By Jakub Pele\v{s}ka, Gustav \v{S}\'ir
MetaRTL is a two-stage framework for relational table learning that first generates lightweight pre-trained table embeddings and then applies non‑parametric message passing to extract meta‑path features. These features are aggregated using an attention module called MetaAttn, shifting computation from deep GNN stacks to efficient meta‑path aggregation. Experiments on 10 real‑world datasets across 24 tasks show that MetaRTL achieves strong performance while reducing computational cost.
By Ken Zhong, Weichen Li, Zheng Wang
arXiv:2606. 03040v1 Announce Type: new Abstract: Relational databases underpin modern enterprise, scientific, and healthcare systems, yet predictive machine learning on such data remains challenging due to their multi-table, heterogeneous, and temporal structure.
By Phillip Jiang
arXiv:2609.01292v1 Announce Type: cross
Abstract: Relational Deep Learning (RDL) has become a powerful paradigm for learning from multi-tabular data. However, manually defining RDL prediction tasks i...
By Oleksii Kolesnichenko, Jakub Pele\v{s}ka, Gustav \v{S}\'{\i}r