WalkBayes: Robust Node Classification via Bayesian Inference and Random Walks under Perturbations

Graph Neural Networks (GNNs) are commonly used for node classification tasks in real-world scenarios. However, research has found that GNN-based classification can significantly drop in performance when the graph structure is perturbed. Current methods, such as adjusting the graph structure to reduce dependency or strengthen protection against perturbations, still have not completely solved this problem. A recent approach using Bayesian inference was introduced to handle perturbations, but this method often faces high uncertainty, which can negatively affect classification results. In this paper, we introduce a new model called WalkBayes, which combines Bayesian inference with random-walk-based label correction to improve the robustness of GNNs in handling complex perturbations. WalkBayes uses random walks to refine label correction, making better use of the graph structure to increase label propagation accuracy and reduce uncertainty. Our experiments on four graph datasets show that WalkBayes performs better than other models in GNN node classification tasks under both random and sparse perturbations to the graph structure.