Task-Agnostic Contrastive Pretraining for Relational Deep Learning

• URL: http://arxiv.org/abs/2506.22530v1
• Date: Fri, 27 Jun 2025 13:18:13 GMT
• Title: Task-Agnostic Contrastive Pretraining for Relational Deep Learning
• Authors: Jakub Peleška, Gustav Šír,
• Abstract summary: We propose a novel task-agnostic contrastive pretraining approach for RDL that enables database-wide representation learning.<n>We implement the respective pretraining approach through a modular RDL architecture.<n>Our preliminary results demonstrate that finetuning the pretrained models measurably outperforms training from scratch.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relational Deep Learning (RDL) is an emerging paradigm that leverages Graph Neural Network principles to learn directly from relational databases by representing them as heterogeneous graphs. However, existing RDL models typically rely on task-specific supervised learning, requiring training separate models for each predictive task, which may hamper scalability and reuse. In this work, we propose a novel task-agnostic contrastive pretraining approach for RDL that enables database-wide representation learning. For that aim, we introduce three levels of contrastive objectives$-$row-level, link-level, and context-level$-$designed to capture the structural and semantic heterogeneity inherent to relational data. We implement the respective pretraining approach through a modular RDL architecture and an efficient sampling strategy tailored to the heterogeneous database setting. Our preliminary results on standard RDL benchmarks demonstrate that fine-tuning the pretrained models measurably outperforms training from scratch, validating the promise of the proposed methodology in learning transferable representations for relational data.

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