  Neural architectures can be naturally viewed as computational graphs.
Motivated by this perspective, we, in this paper, study neural architecture
search (NAS) through the lens of learning random graph models. In contrast to
existing NAS methods which largely focus on searching for a single best
architecture, i.e, point estimation, we propose GraphPNAS a deep graph
generative model that learns a distribution of well-performing architectures.
Relying on graph neural networks (GNNs), our GraphPNAS can better capture
topologies of good neural architectures and relations between operators
therein. Moreover, our graph generator leads to a learnable probabilistic
search method that is more flexible and efficient than the commonly used RNN
generator and random search methods. Finally, we learn our generator via an
efficient reinforcement learning formulation for NAS. To assess the
effectiveness of our GraphPNAS, we conduct extensive experiments on three
search spaces, including the challenging RandWire on TinyImageNet, ENAS on
CIFAR10, and NAS-Bench-101/201. The complexity of RandWire is significantly
larger than other search spaces in the literature. We show that our proposed
graph generator consistently outperforms RNN-based one and achieves better or
comparable performances than state-of-the-art NAS methods.
