---
license: mit
pretty_name: RDB2G-Bench
size_categories:
- 100K<n<1M
task_categories:
- tabular-classification
- tabular-regression
- graph-ml
language:
- en
---
# RDB2G-Bench 

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-black)](https://github.com/chlehdwon/RDB2G-Bench)
[![arXiv](https://img.shields.io/badge/arXiv-2506.01360-b31b1b.svg)](https://arxiv.org/abs/2506.01360)


This is an offical dataset of the paper **RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases.**

RDB2G-Bench is a toolkit for benchmarking graph-based analysis and prediction tasks by converting relational database data into graphs.

Our code is available at [GitHub](https://github.com/chlehdwon/RDB2G-Bench).

## Overview

RDB2G-Bench provides comprehensive performance evaluation data for graph neural network models applied to relational database tasks. The dataset contains extensive experiments across multiple graph configurations and architectures.

## Dataset Summary

Each CSV file contains experimental results with the following columns: 

| Column | Description |
|--------|-------------|
| `idx` | Unique identifier for each experimental configuration |
| `graph` | Binary-encoded string representing the graph structure configuration (e.g., "graph_00000000000010") |
| `train_metric` | Performance metric on the training set (e.g., AUC for classification, MSE for regression, MAP for recommendation) |
| `valid_metric` | Performance metric on the validation set |
| `test_metric` | Performance metric on the test set |
| `params` | Total number of trainable parameters in the model |
| `train_time` | Training time in seconds |
| `valid_time` | Validation time in seconds |
| `test_time` | Testing time in seconds |
| `dataset` | Name of the RDB dataset used (e.g., "rel-avito", "rel-f1") |
| `task` | Name of the prediction task (e.g., "ad-ctr", "user-clicks") |
| `seed` | Random seed for reproducibility |
| `gnn`  | Type of Graph Neural Network (GNN) (e.g., "GraphSAGE", "GIN", "GPS")

### Graph Column Specification

The `graph` column uses binary encoding to represent different edge configurations in the graph structure. Each bit position corresponds to a specific edge type as defined in [`edge_info.json`](https://huggingface.co/datasets/kaistdata/RDB2G-Bench/blob/main/edge_info.json):

- **1**: Edge is connected
- **0**: Edge is disconnected

#### Edge Types:
- **f2p**: Standard foreign key relationships that each table row is transformed into a node (Row2Node).
- **r2e**: Converted relationships that each table row is transformed into an edge (Row2N/E).

The complete edge mapping for each dataset can be found in the [`edge_info.json`](https://huggingface.co/datasets/kaistdata/RDB2G-Bench/blob/main/edge_info.json) file.

## Reference

The dataset construction and implementation of RDB2G-Bench based on [RelBench](https://github.com/snap-stanford/relbench) framework.


## License

This project is distributed under the MIT License as specified in the LICENSE file.