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.

翻译：图神经网络（GNN）的神经架构搜索（NAS），即NAS-GNNs，相比人工设计的GNN架构已取得显著性能提升。然而，这些方法继承了传统NAS方法的问题，例如高计算成本和优化难度。更重要的是，先前的NAS方法忽视了GNN的独特性——GNN无需训练即可具备表达能力。利用随机初始化的权重，我们通过稀疏编码目标函数寻求最优架构参数，由此提出一种新型NAS-GNNs方法，即神经架构编码（NAC）。因此，我们的NAC采用GNN无更新方案，并能在线性时间内高效计算。在多个GNN基准数据集上的实证评估表明，我们的方法达到了最先进的性能，相比强基线方法速度提升高达200倍，准确率提高18.8%。