Beyond Message-Passing: Generalization of Graph Neural Networks via Feature Perturbation for Semi-Supervised Node Classification

Graph neural networks (GNNs) that collect information from neighbors are commonly utilized in semi-supervised learning contexts. In particular, a significant body of research has been dedicated to developing effective graph filters and aggregation methods to filter the information from adjacent nodes. Despite their efficacy, these approaches may encounter challenges due to the sparsity of training nodes, especially when their features are represented as sparse vectors (e.g., bag-of-words). This condition can lead to the overfitting of certain dimensions within the first projection matrix (hyperplane), as the training samples may not adequately represent the full spectrum of learnable parameters. To solve this limitation, we propose an innovative perturbation technique. Specifically, we introduce additional training variability by modifying both the initial features and the hyperplane, which contributes to the reduction of prediction variance by updating the entire dimensions. To the best of our knowledge, our approach is the first to address the overfitting issue in GNNs precipitated by sparse node features. Comprehensive experiments on real-world datasets and ablation studies affirm that our proposed method significantly enhances node classification performance, with improvements of up to 46.5% in GNN algorithms.