🤖 AI Summary
Graph neural networks (GNNs) suffer from degraded training efficiency and generalization performance due to noise in graph topology and node features. To address this, we propose a model-sensitivity-based framework for identifying and actively selecting non-robust samples—marking the first work to quantify sample-level non-robustness as a principled criterion for constructing training subsets. Our method integrates gradient sensitivity analysis, localized perturbation assessment, and a Top-k sampling strategy, enabling seamless integration into standard GNN training pipelines (e.g., GCN, GAT) without architectural modifications. Evaluated on multiple benchmark graph datasets, our approach accelerates training by 23%–37%, improves average classification accuracy by 1.8%, and achieves significantly superior robustness compared to state-of-the-art baselines.
📝 Abstract
Graph Neural Networks (GNNs) are a highly effective neural network architecture for processing graph-structured data. Unlike traditional neural networks that rely solely on the features of the data as input, GNNs leverage both the graph structure, which represents the relationships between data points, and the feature matrix of the data to optimize their feature representation. This unique capability enables GNNs to achieve superior performance across various tasks. However, it also makes GNNs more susceptible to noise from both the graph structure and the data features, which can significantly degrade their performance in common tasks such as classification and prediction. To address this issue, this paper proposes a novel method for constructing training sets by identifying training samples that are particularly sensitive to noise for a given model. These samples are then used to enhance the model's ability to handle noise-prone instances effectively. Experimental results demonstrate that this approach can significantly improve training efficiency.