# 4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs arXiv: https://arxiv.org/abs/2404.18209 · HTML: https://mlsanity.com/paper/2404.18209/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 38 of its 74 references that are in the map. - First posted on arXiv 28 Apr 2024. ## Builds on (most cited first, 30 of 38) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017) - [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018) - [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017) - [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020) - [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017) - [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017) - [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018) - [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021) - [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017) - [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018) - [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020) - [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019) - [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014) - [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020) - [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020) - [OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs](https://mlsanity.com/paper/2103.09430/) (2021) - [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021) - [Molecular Graph Convolutions: Moving Beyond Fingerprints](https://mlsanity.com/paper/1603.00856/) (2016) - [Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1911.07323/) (2019) - [Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks](https://mlsanity.com/paper/2112.14936/) (2021) - [Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction](https://mlsanity.com/paper/2111.00064/) (2021) - [Temporal Graph Benchmark for Machine Learning on Temporal Graphs](https://mlsanity.com/paper/2307.01026/) (2023) - [Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning](https://mlsanity.com/paper/2106.02584/) (2021) - [Decoupling the Depth and Scope of Graph Neural Networks](https://mlsanity.com/paper/2201.07858/) (2022) - [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019) - [Relational Graph Attention Networks](https://mlsanity.com/paper/1904.05811/) (2019) - [Neural Common Neighbor with Completion for Link Prediction](https://mlsanity.com/paper/2302.00890/) (2023) - [Relational Deep Learning: Graph Representation Learning on Relational Databases](https://mlsanity.com/paper/2312.04615/) (2023) ## Built on by (most cited first, 11 of 11) - [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024) - [Griffin: Towards a Graph-Centric Relational Database Foundation Model](https://mlsanity.com/paper/2505.05568/) (2025) - [Turning Tabular Foundation Models into Graph Foundation Models](https://mlsanity.com/paper/2508.20906/) (2025) - [RelBench v2: A Large-Scale Benchmark and Repository for Relational Data](https://mlsanity.com/paper/2602.12606/) (2026) - [Joint Relational Database Generation via Graph-Conditional Diffusion Models](https://mlsanity.com/paper/2505.16527/) (2025) - [Advancing Open and Reproducible Relational Learning: RelArena-α, TabPFN-Rel and RPI](https://mlsanity.com/paper/2608.16319/) (2026) - [Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases](https://mlsanity.com/paper/2603.07916/) (2026) - [RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases](https://mlsanity.com/paper/2606.03040/) (2026) - [PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining](https://mlsanity.com/paper/2607.29129/) (2026) - [The Universal Classifier for Graph Learning](https://mlsanity.com/paper/2609.36302/) (2026) - [RelICL: Training-free Relational Learning with Tabular Foundation Models](https://mlsanity.com/paper/2610.01725/) (2026) ## Abstract (arXiv) Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing. This deficit stems, at least in part, from the lack of established/public RDB benchmarks as needed for training and evaluation purposes. As a result, related model development thus far often defaults to tabular approaches trained on ubiquitous single-table benchmarks, or on the relational side, graph-based alternatives such as GNNs applied to a completely different set of graph datasets devoid of tabular characteristics. To more precisely target RDBs lying at the nexus of these two complementary regimes, we explore a broad class of baseline models predicated on: (i) converting multi-table datasets into graphs using various strategies equipped with efficient subsampling, while preserving tabular characteristics; and (ii) trainable models with well-matched inductive biases that output predictions based on these input subgraphs. Then, to address the dearth of suitable public benchmarks and reduce siloed comparisons, we assemble a diverse collection of (i) large-scale RDB datasets and (ii) coincident predictive tasks. From a delivery standpoint, we operationalize the above four dimensions (4D) of exploration within a unified, scalable open-source toolbox called 4DBInfer. We conclude by presenting evaluations using 4DBInfer, the results of which highlight the importance of considering each such dimension in the design of RDB predictive models, as well as the limitations of more naive approaches such as simply joining adjacent tables. Our source code is released at https://github.com/awslabs/multi-table-benchmark .