Automated Graph Machine Learning: Approaches, Libraries and Directions. (arXiv:2201.01288v1 [cs.LG])

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 …

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