AutoG: Towards automatic graph construction from tabular data

Introduction

Overview

AutoG is a novel framework that addresses the critical challenge of automatically constructing high-quality graphs from tabular data for graph machine learning (GML) applications. While GML has seen tremendous growth, the crucial step of converting tabular data into meaningful graphs remains largely manual and unstandardized. AutoG leverages Large Language Models (LLMs) to automate this process, producing graphs that rival those created by human experts.

Key Features

• Automatic graph schema generation without human intervention
• LLM-based solution for high-quality graph construction

Installation

Step 1: Clone the Repository

git clone https://github.com/amazon-science/Automatic-Table-to-Graph-Generation

cd Automatic-Table-to-Graph-Generation/

Step 2: Install Core Dependencies

# Install 4dbinfer-related libraries
bash multi-table-benchmark/conda/create_conda_env.sh

# Clone DeepJoin to download the language model
git clone https://github.com/mutong184/deepjoin

Step 3: Optional Dependencies

These are required for development but not necessary if using cached LLM outputs:

pip install llama-index-llms-bedrock
pip install llama-index
pip install valentine

Usage

1. Dataset Preparation

Generate the preprocessing dataset:

bash scripts/download.sh

This creates two dataset versions:

• Old Version: A baseline preprocessed version using basic heuristics
• Expert Version: Human expert-generated version with optimized column naming

Note: AutoG uses the 'old' version as input while ignoring the schema information.

2. Running AutoG

To run the AutoG pipeline:

bash scripts/autog.sh

For detailed configuration options, see scripts/autog.sh.

3. Running Graph Machine Learning

Execute GML tasks on the constructed graphs:

bash scripts/run.sh

Using AutoG with Custom Datasets

Follow these steps to apply AutoG to your own data:

1. Generate metadata information:

   from models.llm.gconstruct import analyze_dataframes
   metadata = analyze_dataframes(your_dataframe)

2. Generate initial type predictions:

   from prompts import identify
   types = identify(metadata)

3. Create a DBBRDBDataset wrapper for your data.

4. Generate first-round prompts using AutoG.

Citation

If you use AutoG in your research, please cite:

@inproceedings{
chen2025autog,
title={AutoG: Towards automatic graph construction from tabular data},
author={Zhikai Chen and Han Xie and Jian Zhang and Xiang song and Jiliang Tang and Huzefa Rangwala and George Karypis},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=hovDbX4Gh6}
}