  Neural architecture search (NAS) for Graph neural networks (GNNs), called
NAS-GNNs, has achieved significant performance over manually designed GNN
architectures. However, these methods inherit issues from the conventional NAS
methods, such as high computational cost and optimization difficulty. More
importantly, previous NAS methods have ignored the uniqueness of GNNs, where
GNNs possess expressive power without training. With the randomly-initialized
weights, we can then seek the optimal architecture parameters via the sparse
coding objective and derive a novel NAS-GNNs method, namely neural architecture
coding (NAC). Consequently, our NAC holds a no-update scheme on GNNs and can
efficiently compute in linear time. Empirical evaluations on multiple GNN
benchmark datasets demonstrate that our approach leads to state-of-the-art
performance, which is up to $200\times$ faster and $18.8\%$ more accurate than
the strong baselines.
