  In computer vision research, the process of automating architecture
engineering, Neural Architecture Search (NAS), has gained substantial interest.
Due to the high computational costs, most recent approaches to NAS as well as
the few available benchmarks only provide limited search spaces. In this paper
we propose a surrogate model for neural architecture performance prediction
built upon Graph Neural Networks (GNN). We demonstrate the effectiveness of
this surrogate model on neural architecture performance prediction for
structurally unknown architectures (i.e. zero shot prediction) by evaluating
the GNN on several experiments on the NAS-Bench-101 dataset.
