NeurIPS 2025

Joint Relational Database Generation via
Graph-Conditional Diffusion Models
Mohamed Amine Ketata, David Lüdke, Leo Schwinn, Stephan Günnemann
School of Computation, Information and Technology & Munich Data Science Institute
Technical University of Munich, Germany
Correspondence to: a.ketata@tum.de
Abstract
Building generative models for relational databases (RDBs) is important for many
applications, such as privacy-preserving data release and augmenting real datasets.
However, most prior works either focus on single-table generation or adapt single-
table models to the multi-table setting by relying on autoregressive factorizations
and sequential generation. These approaches limit parallelism, restrict flexibility in
downstream applications, and compound errors due to commonly made conditional
independence assumptions. In this paper, we propose a fundamentally different ap-
proach: jointly modeling all tables in an RDB without imposing any table order. By
using a natural graph representation of RDBs, we propose the Graph-Conditional
Relational Diffusion Model (GRDM), which leverages a graph neural network
to jointly denoise row attributes and capture complex inter-table dependencies.
Extensive experiments on six real-world RDBs demonstrate that our approach sub-
stantially outperforms autoregressive baselines in modeling multi-hop inter-table
correlations and achieves state-of-the-art performance on single-table fidelity met-
rics. Our code is available at https://github.com/ketatam/rdb-diffusion.
1
Introduction
Table A
Table B
Table C
Step 1
Autoregressive
Joint (Ours)
Limiting table order
   
Sequential generation
   
Simplifying assumptions
No table order
     Parallel generation
     Joint modeling of all rows
Step 2
Step 3
Table A
Table B
Table C
Figure 1: Comparison of autoregressive
and joint relational database generation.
Relational databases (RDBs), which organize data into
multiple interlinked tables, are the most widely used
data management system, estim...