Automated machine learning (AutoML) has seen a resurgence in interest with
the boom of deep learning over the past decade. In particular, Neural
Architecture Search (NAS) has seen significant attention throughout the AutoML
research community, and has pushed forward the state-of-the-art in a number of
neural models to address grid-like data such as texts and images. However, very
litter work has been done about Graph Neural Networks (GNN) learning on
unstructured network data. Given the huge number of choices and combinations of
components such as aggregator and activation function, determining the suitable
GNN structure for a specific problem normally necessitates tremendous expert
knowledge and laborious trails. In addition, the slight variation of hyper
parameters such as learning rate and dropout rate could dramatically hurt the
learning capacity of GNN. In this paper, we propose a novel AutoML framework
through the evolution of individual models in a large GNN architecture space
involving both neural structures and learning parameters. Instead of optimizing
only the model structures with fixed parameter settings as existing work, an
alternating evolution process is performed between GNN structures and learning
parameters to dynamically find the best fit of each other. To the best of our
knowledge, this is the first work to introduce and evaluate evolutionary
architecture search for GNN models. Experiments and validations demonstrate
that evolutionary NAS is capable of matching existing state-of-the-art
reinforcement learning approaches for both the semi-supervised transductive and
inductive node representation learning and classification.

本研究提出了一种新的自动化机器学习（AutoML）框架，通过演化图神经网络（GNN）模型来寻找最合适的结构和学习参数，并在半监督和归纳式节点表示学习和分类任务上展示它的性能。