AutoG: Towards automatic graph construction from tabular data

TL;DR

This paper introduces AutoG, an LLM-based method for automatically constructing high-quality graphs from tabular data, addressing a key gap in graph machine learning by formalizing the problem and providing datasets for evaluation.

Contribution

The paper formalizes the graph construction problem from tabular data, introduces datasets for evaluation, and proposes AutoG, an LLM-based approach that automates high-quality graph generation.

Findings

AutoG generates graphs that match human expert quality.

Graph quality significantly impacts downstream task performance.

AutoG outperforms existing methods in various scenarios.

Abstract

Recent years have witnessed significant advancements in graph machine learning (GML), with its applications spanning numerous domains. However, the focus of GML has predominantly been on developing powerful models, often overlooking a crucial initial step: constructing suitable graphs from common data formats, such as tabular data. This construction process is fundamental to applying graph-based models, yet it remains largely understudied and lacks formalization. Our research aims to address this gap by formalizing the graph construction problem and proposing an effective solution. We identify two critical challenges to achieve this goal: 1. The absence of dedicated datasets to formalize and evaluate the effectiveness of graph construction methods, and 2. Existing automatic construction methods can only be applied to some specific cases, while tedious human engineering is required to generate high-quality graphs. To tackle these challenges, we present a two-fold contribution. First, we introduce a set of datasets to formalize and evaluate graph construction methods. Second, we propose an LLM-based solution, AutoG, automatically generating high-quality graph schemas without human intervention. The experimental results demonstrate that the quality of constructed graphs is critical to downstream task performance, and AutoG can generate high-quality graphs that rival those produced by human experts. Our code can be accessible from https://github.com/amazon-science/Automatic-Table-to-Graph-Generation.