RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases

• URL: http://arxiv.org/abs/2506.01360v1
• Date: Mon, 02 Jun 2025 06:34:10 GMT
• Title: RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases
• Authors: Dongwon Choi, Sunwoo Kim, Juyeon Kim, Kyungho Kim, Geon Lee, Shinhwan Kang, Myunghwan Kim, Kijung Shin,
• Abstract summary: RDB-to-graph modeling helps capture cross-table dependencies, leading to enhanced performance across diverse tasks.<n>Applying a common rule for graph modeling leads to a 10% drop in performance compared to the best-performing graph model.<n>We introduce RDB2G, the first benchmark framework for evaluating such methods.
• Score: 23.836665904554426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relational databases (RDBs) are composed of interconnected tables, where relationships between them are defined through foreign keys. Recent research on applying machine learning to RDBs has explored graph-based representations of RDBs, where rows of tables are modeled as nodes, and foreign key relationships are modeled as edges. RDB-to-graph modeling helps capture cross-table dependencies, ultimately leading to enhanced performance across diverse tasks. However, there are numerous ways to model RDBs as graphs, and performance varies significantly depending on the chosen graph model. In our analysis, applying a common heuristic rule for graph modeling leads to up to a 10% drop in performance compared to the best-performing graph model, which remains non-trivial to identify. To foster research on intelligent RDB-to-graph modeling, we introduce RDB2G-Bench, the first benchmark framework for evaluating such methods. We construct extensive datasets covering 5 real-world RDBs and 12 predictive tasks, resulting in around 50k graph-performance pairs for efficient and reproducible evaluations. Thanks to our precomputed datasets, we were able to benchmark 9 automatic RDB-to-graph modeling methods on the 12 tasks over 600x faster than on-the-fly evaluation, which requires repeated model training. Our analysis of the datasets and benchmark results reveals key structural patterns affecting graph model effectiveness, along with practical implications for effective graph modeling.

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