Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph
learning tasks, yet their reliance on message-passing constraints their
deployment in latency-sensitive applications such as financial fraud detection.
Recent works have explored distilling knowledge from GNNs to Multi-Layer
Perceptrons (MLPs) to accelerate inference. However, this task-specific
supervised distillation limits generalization to unseen nodes, which are
prevalent in latency-sensitive applications. To this end, we present
\textbf{\textsc{SimMLP}}, a \textbf{\textsc{Sim}}ple yet effective framework
for learning \textbf{\textsc{MLP}}s on graphs without supervision, to enhance
generalization. \textsc{SimMLP} employs self-supervised alignment between GNNs
and MLPs to capture the fine-grained and generalizable correlation between node
features and graph structures, and proposes two strategies to alleviate the
risk of trivial solutions. Theoretically, we comprehensively analyze
\textsc{SimMLP} to demonstrate its equivalence to GNNs in the optimal case and
its generalization capability. Empirically, \textsc{SimMLP} outperforms
state-of-the-art baselines, especially in settings with unseen nodes. In
particular, it obtains significant performance gains {\bf (7$\sim$26\%)} over
MLPs and inference acceleration over GNNs {\bf (90$\sim$126$\times$)} on
large-scale graph datasets. Our codes are available at:
https://github.com/Zehong-Wang/SimMLP.

通过无监督学习简化后的多层感知器（MLPs）在图表上进行学习，以增强泛化效果，特别是在未见节点的设置中，实现了显著的性能提升（7-26%）和图表推断加速（90-126 倍），在大规模图表数据集上优于现有基准方案。