# GFS: Graph-based Feature Synthesis for Prediction over Relational Databases

arXiv: https://arxiv.org/abs/2312.02037 · HTML: https://mlsanity.com/paper/2312.02037/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [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)
- [On the Bottleneck of Graph Neural Networks and its Practical Implications](https://mlsanity.com/paper/2006.05205/) (2020)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Relational Graph Attention Networks](https://mlsanity.com/paper/1904.05811/) (2019)
- [Supervised Learning on Relational Databases with Graph Neural Networks](https://mlsanity.com/paper/2002.02046/) (2020)
- [Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data](https://mlsanity.com/paper/1807.00311/) (2018)
- [XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning](https://mlsanity.com/paper/1912.00290/) (2019)
- [TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data](https://mlsanity.com/paper/2106.03096/) (2021)
- [One button machine for automating feature engineering in relational databases](https://mlsanity.com/paper/1706.00327/) (2017)
- [Capturing Row and Column Semantics in Transformer Based Question Answering over Tables](https://mlsanity.com/paper/2104.08303/) (2021)
- [End-to-end Optimization of Machine Learning Prediction Queries](https://mlsanity.com/paper/2206.00136/) (2022)

## Built on by (most cited first, 16 of 16)

- [Griffin: Towards a Graph-Centric Relational Database Foundation Model](https://mlsanity.com/paper/2505.05568/) (2025)
- [Relational In-Context Learning via Synthetic Pre-training with Structural Prior](https://mlsanity.com/paper/2603.03805/) (2026)
- [Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions](https://mlsanity.com/paper/2401.02143/) (2024)
- [4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs](https://mlsanity.com/paper/2404.18209/) (2024)
- [No Need to Train Your RDB Foundation Model](https://mlsanity.com/paper/2602.13697/) (2026)
- [Transformers Meet Relational Databases](https://mlsanity.com/paper/2412.05218/) (2024)
- [REDELEX: A Framework for Relational Deep Learning Exploration](https://mlsanity.com/paper/2506.22199/) (2025)
- [From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs](https://mlsanity.com/paper/2506.02243/) (2025)
- [Task-Agnostic Contrastive Pretraining for Relational Deep Learning](https://mlsanity.com/paper/2506.22530/) (2025)
- [A Pre-training Framework for Relational Data with Information-theoretic Principles](https://mlsanity.com/paper/2507.09837/) (2025)
- [RCCFormer: A Robust Crowd Counting Network Based on Transformer](https://mlsanity.com/paper/2504.04935/) (2025)
- [Database Views as Explanations for Relational Deep Learning](https://mlsanity.com/paper/2509.09482/) (2025)
- [Relational Database Distillation: From Structured Tables to Condensed Graph Data](https://mlsanity.com/paper/2510.06980/) (2025)
- [Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases](https://mlsanity.com/paper/2508.08327/) (2025)
- [From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics](https://mlsanity.com/paper/2605.14464/) (2026)
- [InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs](https://mlsanity.com/paper/2607.05478/) (2026)

## Abstract (arXiv)

Relational databases are extensively utilized in a variety of modern information system applications, and they always carry valuable data patterns. There are a huge number of data mining or machine learning tasks conducted on relational databases. However, it is worth noting that there are limited machine learning models specifically designed for relational databases, as most models are primarily tailored for single table settings. Consequently, the prevalent approach for training machine learning models on data stored in relational databases involves performing feature engineering to merge the data from multiple tables into a single table and subsequently applying single table models. This approach not only requires significant effort in feature engineering but also destroys the inherent relational structure present in the data. To address these challenges, we propose a novel framework called Graph-based Feature Synthesis (GFS). GFS formulates the relational database as a heterogeneous graph, thereby preserving the relational structure within the data. By leveraging the inductive bias from single table models, GFS effectively captures the intricate relationships inherent in each table. Additionally, the whole framework eliminates the need for manual feature engineering. In the extensive experiment over four real-world multi-table relational databases, GFS outperforms previous methods designed for relational databases, demonstrating its superior performance.