{"type":"ScholarlyArticle","title":"Automated Machine Learning on Graphs: A Survey","authors":[{"name":"Ziwei Zhang","slug":"ziwei-zhang","given":"Ziwei","family":"Zhang","affiliation":""},{"name":"Xin Wang","slug":"xin-wang","given":"Xin","family":"Wang","affiliation":""},{"name":"Wenwu Zhu","slug":"wenwu-zhu","given":"Wenwu","family":"Zhu","affiliation":""}],"abstract":"Machine learning on graphs has been extensively studied\tin 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 attention 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. This paper is the first systematic and comprehensive review of automated machine learning on graphs to the best of our knowledge.","doi":"10.24963/ijcai.2021/637","url":"https://ijcai.org/proceedings/2021/637","pdf":"https://ijcai.org/proceedings/2021/0637.pdf","markdown":"https://ijcai.org/proceedings/2021/637.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/637","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Survey Track","track":"Survey Track","number":637,"pages":{"first":4704,"last":4712},"keywords":["Machine learning: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}