Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions

TL;DR

This paper provides a comprehensive overview of automated graph machine learning, including approaches, libraries, benchmarks, and future research directions, highlighting AutoGL as a key open-source tool.

Contribution

It is the first systematic review covering approaches, libraries, and future directions in automated graph machine learning, and introduces AutoGL, the first dedicated open-source library.

Findings

AutoGL is the first open-source library for automated graph machine learning.

A tailored benchmark supports unified and reproducible evaluations.

The paper discusses future research directions in the field.

Abstract

Graph machine learning has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To tackle the challenge, automated graph machine learning, which aims at discovering the best hyper-parameter and neural architecture configuration for different graph tasks/data without manual design, is gaining an increasing number of attentions from the research community. In this paper, we extensively discuss automated graph machine learning approaches, covering hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We briefly overview existing libraries designed for either graph machine learning or automated machine learning respectively, and further in depth introduce AutoGL, our dedicated and the world's first open-source library for automated graph machine learning. Also, we describe a tailored benchmark that supports unified, reproducible, and efficient evaluations. Last but not least, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive discussion of approaches, libraries as well as directions for automated graph machine learning.