  In computer vision research, the process of automating architecture
engineering, Neural Architecture Search (NAS), has gained substantial interest.
In the past, NAS was hardly accessible to researchers without access to
large-scale compute systems, due to very long compute times for the recurrent
search and evaluation of new candidate architectures. The NAS-Bench-101 dataset
facilitates a paradigm change towards classical methods such as supervised
learning to evaluate neural architectures. In this paper, we propose a graph
encoder built upon Graph Neural Networks (GNN). We demonstrate the
effectiveness of the proposed encoder on NAS performance prediction for seen
architecture types as well an unseen ones (i.e., zero shot prediction). We also
provide a new variational-sequential graph autoencoder (VS-GAE) based on the
proposed graph encoder. The VS-GAE is specialized on encoding and decoding
graphs of varying length utilizing GNNs. Experiments on different sampling
methods show that the embedding space learned by our VS-GAE increases the
stability on the accuracy prediction task.
