  Graph Neural Networks (GNNs) have revolutionized graph-based machine
learning, but their heavy computational demands pose challenges for
latency-sensitive edge devices in practical industrial applications. In
response, a new wave of methods, collectively known as GNN-to-MLP Knowledge
Distillation, has emerged. They aim to transfer GNN-learned knowledge to a more
efficient MLP student, which offers faster, resource-efficient inference while
maintaining competitive performance compared to GNNs. However, these methods
face significant challenges in situations with insufficient training data and
incomplete test data, limiting their applicability in real-world applications.
To address these challenges, we propose AdaGMLP, an AdaBoosting GNN-to-MLP
Knowledge Distillation framework. It leverages an ensemble of diverse MLP
students trained on different subsets of labeled nodes, addressing the issue of
insufficient training data. Additionally, it incorporates a Node Alignment
technique for robust predictions on test data with missing or incomplete
features. Our experiments on seven benchmark datasets with different settings
demonstrate that AdaGMLP outperforms existing G2M methods, making it suitable
for a wide range of latency-sensitive real-world applications. We have
submitted our code to the GitHub repository
(https://github.com/WeigangLu/AdaGMLP-KDD24).
