AutoGL: A Library for Automated Graph Learning

Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, Wenwu Zhu

Keywords: GNN, AutoML, HPO, Feature Engineering, Ensemble, library

TL;DR: We present AutoGL, the first library for automated machine learning on graphs, which is open-source, easy to use, and flexible to be extended.

Abstract: Recent years have witnessed an upsurge of research interests and applications of machine learning on graphs. Automated machine learning (AutoML) on graphs is on the horizon to automatically design the optimal machine learning algorithm for a given graph task. However, none of the existing libraries can fully support AutoML on graphs. To fill this gap, we present Automated Graph Learning (AutoGL), the first library for automated machine learning on graphs. AutoGL is open-source, easy to use, and flexible to be extended. Specifically, we propose an automated machine learning pipeline for graph data containing four modules: auto feature engineering, model training, hyper-parameter optimization, and auto ensemble. For each module, we provide numerous state-of-the-art methods and flexible base classes and APIs, which allow easy customization. We further provide experimental results to showcase the usage of our AutoGL library.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2104.04987/code)