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

Published: 31 Dec 2025, Last Modified: 31 Dec 2025Accepted by TMLREveryoneRevisionsBibTeXCC BY 4.0
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, although the benefits of split-parallel training diminish on smaller hypergraphs with fewer nodes due to communication overhead, it achieves substantial speedups of up to 3.18× on large-scale relational datasets and up to 2.94× on large hypergraph datasets.
Submission Length: Long submission (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.
Code: https://github.com/tfahim15/rel-HNN
Supplementary Material: zip
Assigned Action Editor: ~Giannis_Nikolentzos1
Submission Number: 5751
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