Graph Conditional Flow Matching for Relational Data Generation

2025-05-21

AI Summary

Existing multi-table data generation methods struggle to model long-range dependencies and complex foreign-key structures—such as multi-parent tables and heterogeneous associations. To address this, we propose a graph-conditional flow matching framework, the first to introduce flow matching into relational data synthesis. Our approach employs a graph neural network (GNN) to encode the foreign-key dependency graph and guides a record-level denoising neural network, enabling joint, database-wide generation. Crucially, it supports dynamic, cross-table information propagation within arbitrarily connected components, balancing structural flexibility with rich semantic expressiveness. Evaluated on multiple benchmark datasets, our method achieves state-of-the-art synthetic fidelity, significantly improving consistency in multi-table association patterns and joint statistical distributions compared to prior work.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Data synthesis is gaining momentum as a privacy-enhancing technology. While single-table tabular data generation has seen considerable progress, current methods for multi-table data often lack the flexibility and expressiveness needed to capture complex relational structures. In particular, they struggle with long-range dependencies and complex foreign-key relationships, such as tables with multiple parent tables or multiple types of links between the same pair of tables. We propose a generative model for relational data that generates the content of a relational dataset given the graph formed by the foreign-key relationships. We do this by learning a deep generative model of the content of the whole relational database by flow matching, where the neural network trained to denoise records leverages a graph neural network to obtain information from connected records. Our method is flexible, as it can support relational datasets with complex structures, and expressive, as the generation of each record can be influenced by any other record within the same connected component. We evaluate our method on several benchmark datasets and show that it achieves state-of-the-art performance in terms of synthetic data fidelity.

Problem

• Generating multi-table data with complex relational structures
• Handling long-range dependencies and foreign-key relationships
• Improving synthetic data fidelity for relational datasets

Innovation

• Graph neural network for relational data generation
• Flow matching for deep generative modeling
• Handling complex foreign-key relationships flexibly