  Bayesian optimization (BO) is an effective method of finding the global
optima of black-box functions. Recently BO has been applied to neural
architecture search and shows better performance than pure evolutionary
strategies. All these methods adopt Gaussian processes (GPs) as surrogate
function, with the handcraft similarity metrics as input. In this work, we
propose a Bayesian graph neural network as a new surrogate, which can
automatically extract features from deep neural architectures, and use such
learned features to fit and characterize black-box objectives and their
uncertainty. Based on the new surrogate, we then develop a graph Bayesian
optimization framework to address the challenging task of deep neural
architecture search. Experiment results show our method significantly
outperforms the comparative methods on benchmark tasks.
