Automated Machine Learning on Graphs: A Survey

• URL: http://arxiv.org/abs/2103.00742v1
• Date: Mon, 1 Mar 2021 04:20:33 GMT
• Title: Automated Machine Learning on Graphs: A Survey
• Authors: Ziwei Zhang, Xin Wang and Wenwu Zhu
• Abstract summary: This paper is the first systematic and comprehensive review of automated machine learning on graphs. We focus on hyper- parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. In the end, we share our insights on future research directions for automated graph machine learning.
• Score: 81.21692888288658
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning on graphs 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 solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attentions from the research community. Therefore, we comprehensively survey AutoML on graphs in this paper, primarily focusing on hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We further overview libraries related to automated graph machine learning and in depth discuss AutoGL, the first dedicated open-source library for AutoML on graphs. In the end, we share our insights on future research directions for automated graph machine learning. To the best of our knowledge, this paper is the first systematic and comprehensive review of automated machine learning on graphs.

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