We revisit recent spectral GNN approaches to semi-supervised node classification (SSNC). We posit that state-of-the-art (SOTA) GNN architectures may be over-engineered for common SSNC benchmark datasets (citation networks, page-page networks, etc.). By replacing feature aggregation with a non-parametric learner we are able to streamline the GNN design process and avoid many of the engineering complexities associated with SOTA hyperparameter selection (GNN depth, non-linearity choice, feature dropout probability, etc.). Our empirical experiments suggest conventional methods such as non-parametric regression are well suited for semi-supervised learning on sparse, directed networks and a variety of other graph types commonly found in SSNC benchmarks. Additionally, we bring attention to recent changes in evaluation conventions for SSNC benchmarking and how this may have partially contributed to rising performances over time.

翻译：我们重新审视了近期用于半监督节点分类（SSNC）的谱图神经网络方法。我们认为，针对常见的SSNC基准数据集（引文网络、网页-网页网络等），当前最先进的图神经网络架构可能存在过度设计的问题。通过将特征聚合替换为非参数学习器，我们能够简化图神经网络的设计流程，并避免与最先进模型超参数选择相关的诸多工程复杂性（如图神经网络深度、非线性函数选择、特征丢弃概率等）。我们的实证实验表明，传统方法（如非参数回归）非常适用于稀疏有向网络以及SSNC基准测试中常见的各类图结构上的半监督学习。此外，我们提请关注近期SSNC基准测试评估规范的变化，并探讨这些变化如何在一定程度上推动了模型性能的持续提升。