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Neural architecture search (NAS) for Graph neural networks (GNNs), calledNAS-GNNs, has achieved significant performance over manually designed GNNarchitectures. However, these methods inherit issues from the conventional NASmethods, such as high computational cost and optimization difficulty. Moreimportantly, previous NAS methods have ignored the uniqueness of GNNs, whereGNNs possess expressive power without training. With the randomly-initializedweights, we can then seek the optimal architecture parameters via the sparsecoding objective and derive a novel NAS-GNNs method, namely neural architecturecoding (NAC). Consequently, our NAC holds a no-update scheme on GNNs and canefficiently compute in linear time. Empirical evaluations on multiple GNNbenchmark datasets demonstrate that our approach leads to state-of-the-artperformance, which is up to $200\times$ faster and $18.8\%$ more accurate thanthe strong baselines.

Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks