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:虽然图形神经网络(GNNS)在处理非欧洲语言结构数据方面显示了其效力,但由于多霍数据依赖性造成的可缩缩性限制,很难在实际应用中应用这些数据。现有方法试图通过培训多层超视仪(MLP),专门使用来自经过培训的GNS的标签,在节点内容特征上进行多层超光谱(MLP)培训,以解决可缩放问题。即使MLP的性能可以大大改进,但有两个问题使MLP公司无法超过GNNS的性能,并在实践中加以使用:对图形结构信息的无知和对节点地物噪音的敏感性。在本文件中,我们提议学习NOise-robust结构对MLPs(NOSOGOG)的认知 MLPs,以克服挑战。具体地,我们首先用位置特性来补充节点内容,帮助MLPs捕获图结构信息。我们随后设计了一个新的代表性相似性蒸馏战略,将结构的节点与MNPPS/MPs。最后,我们引入对抗性功能增强功能,以确保在SMSM-O的噪音噪音和MLA-ODMS-stal的运行中进行稳定的学习。</s>