# Row Conditional-TGAN for generating synthetic relational databases

arXiv: https://arxiv.org/abs/2211.07588 · HTML: https://mlsanity.com/paper/2211.07588/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [Adversarial Learning for Neural Dialogue Generation](https://mlsanity.com/paper/1701.06547/) (2017)
- [Synthesizing Tabular Data using Generative Adversarial Networks](https://mlsanity.com/paper/1811.11264/) (2018)
- [Generative Synthesis of Insurance Datasets](https://mlsanity.com/paper/1912.02423/) (2019)

## Built on by (most cited first, 15 of 15)

- [REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers](https://mlsanity.com/paper/2302.02041/) (2023)
- [A Comprehensive Survey of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2504.16506/) (2025)
- [PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models](https://mlsanity.com/paper/2602.04029/) (2026)
- [TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data](https://mlsanity.com/paper/2501.12012/) (2025)
- [Relational In-Context Learning via Synthetic Pre-training with Structural Prior](https://mlsanity.com/paper/2603.03805/) (2026)
- [Benchmarking the Fidelity and Utility of Synthetic Relational Data](https://mlsanity.com/paper/2410.03411/) (2024)
- [RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models](https://mlsanity.com/paper/2506.00710/) (2025)
- [IRG: Modular Synthetic Relational Database Generation with Complex Relational Schemas](https://mlsanity.com/paper/2312.15187/) (2023)
- [Graph-Conditional Flow Matching for Relational Data Generation](https://mlsanity.com/paper/2505.15668/) (2025)
- [Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting](https://mlsanity.com/paper/2602.07126/) (2026)
- [Democratizing Tabular Data Access with an Openunicodex2013Source Syntheticunicodex2013Data SDK](https://mlsanity.com/paper/2508.00718/) (2025)
- [Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing](https://mlsanity.com/paper/2507.06996/) (2025)
- [Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss](https://mlsanity.com/paper/2606.31904/) (2026)
- [Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data](https://mlsanity.com/paper/2607.15606/) (2026)
- [Beyond Marginals: A Multi-Dimensional Evaluation Framework for Multi-Table Synthetic Data Generation](https://mlsanity.com/paper/2610.06854/) (2026)

## Abstract (arXiv)

Besides reproducing tabular data properties of standalone tables, synthetic relational databases also require modeling the relationships between related tables. In this paper, we propose the Row Conditional-Tabular Generative Adversarial Network (RC-TGAN), a novel generative adversarial network (GAN) model that extends the tabular GAN to support modeling and synthesizing relational databases. The RC-TGAN models relationship information between tables by incorporating conditional data of parent rows into the design of the child table's GAN. We further extend the RC-TGAN to model the influence that grandparent table rows may have on their grandchild rows, in order to prevent the loss of this connection when the rows of the parent table fail to transfer this relationship information. The experimental results, using eight real relational databases, show significant improvements in the quality of the synthesized relational databases when compared to the benchmark system, demonstrating the effectiveness of the RC-TGAN in preserving relationships between tables of the original database.