# REDELEX: A Framework for Relational Deep Learning Exploration

arXiv: https://arxiv.org/abs/2506.22199 · HTML: https://mlsanity.com/paper/2506.22199/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [UniT: Multimodal Multitask Learning with a Unified Transformer](https://mlsanity.com/paper/2102.10772/) (2021)
- [Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions](https://mlsanity.com/paper/2209.03430/) (2022)
- [RelBench: A Benchmark for Deep Learning on Relational Databases](https://mlsanity.com/paper/2407.20060/) (2024)
- [Supervised Learning on Relational Databases with Graph Neural Networks](https://mlsanity.com/paper/2002.02046/) (2020)
- [RelGNN: Composite Message Passing for Relational Deep Learning](https://mlsanity.com/paper/2502.06784/) (2025)
- [Turning 30: New Ideas in Inductive Logic Programming](https://mlsanity.com/paper/2002.11002/) (2020)
- [Trompt: Towards a Better Deep Neural Network for Tabular Data](https://mlsanity.com/paper/2305.18446/) (2023)
- [GFS: Graph-based Feature Synthesis for Prediction over Relational Databases](https://mlsanity.com/paper/2312.02037/) (2023)

## Built on by (most cited first, 8 of 8)

- [RelBench v2: A Large-Scale Benchmark and Repository for Relational Data](https://mlsanity.com/paper/2602.12606/) (2026)
- [Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learning](https://mlsanity.com/paper/2511.04557/) (2025)
- [Advancing Open and Reproducible Relational Learning: RelArena-α, TabPFN-Rel and RPI](https://mlsanity.com/paper/2608.16319/) (2026)
- [Declarative by Design, Assistable Only by Convention: Benchmarking Multi-Agent Frameworks for AI-Assistability](https://mlsanity.com/paper/2602.11198/) (2026)
- [Universal Encoders for Modular Relational Deep Learning](https://mlsanity.com/paper/2606.21434/) (2026)
- [Incremental Evaluation and Training in Relational Deep Learning](https://mlsanity.com/paper/2608.13023/) (2026)
- [Relational Task Generation Language: A Declarative Specification Framework for Relational Deep Learning](https://mlsanity.com/paper/2609.01292/) (2026)
- [RelICL: Training-free Relational Learning with Tabular Foundation Models](https://mlsanity.com/paper/2610.01725/) (2026)

## Abstract (arXiv)

Relational databases (RDBs) are widely regarded as the gold standard for storing structured information. Consequently, predictive tasks leveraging this data format hold significant application promise. Recently, Relational Deep Learning (RDL) has emerged as a novel paradigm wherein RDBs are conceptualized as graph structures, enabling the application of various graph neural architectures to effectively address these tasks. However, given its novelty, there is a lack of analysis into the relationships between the performance of various RDL models and the characteristics of the underlying RDBs. In this study, we present REDELEX-a comprehensive exploration framework for evaluating RDL models of varying complexity on the most diverse collection of over 70 RDBs, which we make available to the community. Benchmarked alongside key representatives of classic methods, we confirm the generally superior performance of RDL while providing insights into the main factors shaping performance, including model complexity, database sizes and their structural properties.