RelDiff

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RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models

RelDiff 최신버젼 다운로드

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# RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models

This repository provides the official implementation of the paper "RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models".

## Latest Update

- [2025.]：Our code is at the final stage of cleaning up. Please check back soon for its release!

## Introduction

<div align="center">
  <img src="images/reldiff_flowchart.svg" alt="RelDiff Pipeline" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
  <p><em>Figure 1: A high-level overview of RelDiff</a></em></p>
</div>
RelDiff is a novel generative framework for synthesizing relational databases with arbitrarily complex schemas, achieving high fidelity and utility. Its key innovations include:

1) A principled framework for **generating foreign key structures** in relational databases, incorporating hard constraints for **referential integrity** via Bayesian stochastic block models.
2) A **joint diffusion model** for synthesizing mixed-type attributes, utilizing GNNs to capture global **inter-table dependencies**.
3) Explicitly modeling **dimension tables** as a distinct data type and defining our **diffusion model in data space**.

The schema of RelDiff is presented in the figure above.

RelDiff

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RelDiff 최신버젼 다운로드

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