  Owing to the remarkable capability of extracting effective graph embeddings,
graph convolutional network (GCN) and its variants have been successfully
applied to a broad range of tasks, such as node classification, link
prediction, and graph classification. Traditional GCN models suffer from the
issues of overfitting and oversmoothing, while some recent techniques like
DropEdge could alleviate these issues and thus enable the development of deep
GCN. However, training GCN models is non-trivial, as it is sensitive to the
choice of hyperparameters such as dropout rate and learning weight decay,
especially for deep GCN models. In this paper, we aim to automate the training
of GCN models through hyperparameter optimization. To be specific, we propose a
self-tuning GCN approach with an alternate training algorithm, and further
extend our approach by incorporating the population based training scheme.
Experimental results on three benchmark datasets demonstrate the effectiveness
of our approaches on optimizing multi-layer GCN, compared with several
representative baselines.
