论文标题
部分可观测时空混沌系统的无模型预测
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
论文作者
论文摘要
在学术和行业中，已经对机器学习进行了广泛的研究。但是，随着关于图形学习繁荣的文献，具有大量的新兴方法和技术，对于不同的与图相关的任务，手动设计最佳机器学习算法变得越来越困难。为了应对挑战，旨在为不同的图形任务/数据/数据而无需手动设计的自动图机学习，旨在发现最佳的超参数和神经体系结构配置。在本文中，我们广泛讨论了自动化图机学习方法，涵盖了用于图机学习的高参数优化（HPO）和神经体系结构搜索（NAS）。我们简要概述了分别为图形机学习或自动化机器学习设计的现有库，并深入介绍了AutoGl，我们的专用和世界上第一个用于自动化图机器学习的开源库。另外，我们描述了一个量身定制的基准测试，该基准支持统一，可重复和有效的评估。最后但并非最不重要的一点是，我们分享了对自动图机学习的未来研究方向的见解。本文是对方法，库以及自动图机学习的方向的第一个系统，全面的讨论。
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.
