Abstract
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.