Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases

TMLR Paper5751 Authors

Abstract: Relational databases (RDBs) are ubiquitous in enterprise and real-world applications. Flattening the database poses challenges for deep learning models that rely on fixed-size input representations to capture relational semantics from the structured nature of relational data. Graph neural networks (GNNs) have been proposed to address this, but they often oversimplify relational structures by modeling all the tuples as monolithic nodes and ignoring intra-tuple associations. In this work, we propose a novel hypergraph-based framework, that we call rel-HNN, which models each unique attribute-value pair as a node and each tuple as a hyperedge, enabling the capture of fine-grained intra-tuple relationships. Our approach learns explicit multi-level representations across attribute-value, tuple, and table levels. To address the scalability challenges posed by large RDBs, we further introduce a split-parallel training algorithm that leverages multi-GPU execution for efficient hypergraph learning. Extensive experiments on real-world and benchmark datasets demonstrate that rel-HNN significantly outperforms existing methods in both classification and regression tasks. Moreover, our split-parallel training achieves substantial speedups—up to 3.18x for learning on relational data and up to 2.94x for hypergraph learning—compared to conventional single-GPU execution.

Submission Length: Regular submission (no more than 12 pages of main content)

Previous TMLR Submission Url: https://openreview.net/forum?id=0foDCA36Ff

Changes Since Last Submission: We received a note that the GitHub link in the submission was not anonymous. We have removed the link from the paper and attached the source code as supplementary material.

Assigned Action Editor: ~Giannis_Nikolentzos1

Submission Number: 5751