Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions

arXiv:2201.01288v2 Announce Type: replace
Abstract: 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 …

abstract academic algorithm arxiv automated benchmarks cs.ai cs.lg design graph graph learning however industry libraries literature machine machine learning tasks type vast