用图扩散模型生成真实关系型数据库，保持结构与属性一致。
RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models
- 基于图扩散建模表间外键结构，联合生成属性与关系。
- 在11个数据集上优于现有方法，生成数据更真实、完整。
- 适合需要高质量合成数据的科研与工业场景。
现实世界中的数据库多为关系型，由多个相互关联的表组成，包含复杂的结构与统计依赖。在关系型数据上构建生成模型，在生成合成数据和填补缺失值方面展现出巨大潜力。然而，现有方法常难以捕捉这种复杂性，通常将关系型数据简化为条件生成的扁平表，并施加限制性结构假设。为此，我们提出RelDiff，一种新颖的扩散生成模型，通过显式建模外键图结构，合成完整的关 系型数据库。RelDiff结合了跨所有表的联合图条件扩散过程以生成属性，以及基于随机块模型（Stochastic Block Model, SBM）的2K+SBM图生成器以生成结构。将图结构与关系属性解耦，确保高保真度与引用完整性，这两点对合成关系型数据库至关重要。在11个基准数据集上的实验表明，RelDiff在生成逼真且连贯的合成关系型数据库方面持续优于先前方法。代码已公开于https://github.com/ValterH/RelDiff。
原文摘要 · Abstract (English)
Real-world databases are predominantly relational, comprising multiple interlinked tables that contain complex structural and statistical dependencies. Learning generative models on relational data has shown great promise in generating synthetic data and imputing missing values. However, existing methods often struggle to capture this complexity, typically reducing relational data to conditionally generated flat tables and imposing limiting structural assumptions. To address these limitations, we introduce RelDiff, a novel diffusion generative model that synthesizes complete relational databases by explicitly modeling their foreign key graph structure. RelDiff combines a joint graph-conditioned diffusion process across all tables for attribute synthesis, and a $2K+$SBM graph generator based on the Stochastic Block Model for structure generation. The decomposition of graph structure and relational attributes ensures both high fidelity and referential integrity, both of which are crucial aspects of synthetic relational database generation. Experiments on 11 benchmark datasets demonstrate that RelDiff consistently outperforms prior methods in producing realistic and coherent synthetic relational databases. Code is available at https://github.com/ValterH/RelDiff.
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