While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-Euclidean structural data, they are difficult to be deployed in real applications due to the scalability constraint imposed by multi-hop data dependency. Existing methods attempt to address this scalability issue by training multi-layer perceptrons (MLPs) exclusively on node content features using labels derived from trained GNNs. Even though the performance of MLPs can be significantly improved, two issues prevent MLPs from outperforming GNNs and being used in practice: the ignorance of graph structural information and the sensitivity to node feature noises. In this paper, we propose to learn NOise-robust Structure-aware MLPs On Graphs (NOSMOG) to overcome the challenges. Specifically, we first complement node content with position features to help MLPs capture graph structural information. We then design a novel representational similarity distillation strategy to inject structural node similarities into MLPs. Finally, we introduce the adversarial feature augmentation to ensure stable learning against feature noises and further improve performance. Extensive experiments demonstrate that NOSMOG outperforms GNNs and the state-of-the-art method in both transductive and inductive settings across seven datasets, while maintaining a competitive inference efficiency. Codes are available at https://github.com/meettyj/NOSMOG.

翻译：摘要：尽管图神经网络（GNNs）在处理非欧几里得结构数据方面展现了其有效性，但由于多跳数据依赖所带来的可扩展性限制，它们难以在真实应用中部署。现有方法试图通过利用从训练好的GNNs获得的标签，仅基于节点内容特征训练多层感知机（MLPs）来解决这一可扩展性问题。尽管MLPs的性能可以显著提升，但有两个问题阻碍了MLPs超越GNNs并实际应用：对图结构信息的忽视以及对节点特征噪声的敏感性。本文提出学习**图上的抗噪声且结构感知的MLPs（NOSMOG）**以克服这些挑战。具体而言，我们首先用位置特征补充节点内容，以帮助MLPs捕获图结构信息。然后，我们设计了一种新颖的表征相似性蒸馏策略，将结构节点相似性注入MLPs。最后，我们引入对抗性特征增强，以确保对特征噪声的稳定学习并进一步提升性能。大量实验表明，NOSMOG在七个数据集的直推式和归纳式设置下均优于GNNs及当前最优方法，同时保持有竞争力的推理效率。代码可在 https://github.com/meettyj/NOSMOG 获取。