AutoGL swMATH ID: 38082 Software Authors: Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, Wenwu Zhu Description: AutoGL: A Library for Automated Graph Learning. 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, all current libraries cannot support AutoML on graphs. To tackle this problem, 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. Homepage: https://arxiv.org/abs/2104.04987 Source Code: https://github.com/THUMNLab/AutoGL Dependencies: Python Keywords: Machine Learning; arXiv_cs.LG; Artificial Intelligence; arXiv_cs.AI; AutoGL; Automated Graph Learning; Python Related Software: AutoKeras; LightGBM; PyTorch-BigGraph; NetLSD; AutoNE; DGL; Eigen-GNN; AliGraph; PyTorch; auto-sklearn; Python Cited in: 0 Documents Standard Articles 1 Publication describing the Software Year 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 2021