Graph Neural Networks (GNNs) have established themselves as one of the most powerful neural network architectures, excelling in leveraging graph topology and node features for various tasks. However, GNNs are inherently vulnerable to noise in their inputs. Such noise can significantly degrade their performance. To address this challenge, we propose a novel approach that employs adversarial robustness evaluation techniques to identify nodes in the graph that are most susceptible to noise. By selecting and constructing a training set composed of these particularly noise-prone nodes, we then use them to train a Graph Convolutional Network (GCN). Our experimental results demonstrate that this strategy leads to substantial improvements in the GCN's performance.

翻译：图神经网络（GNNs）已成为最强大的神经网络架构之一，擅长利用图拓扑结构和节点特征处理各类任务。然而，GNNs本质上对其输入中的噪声较为敏感，此类噪声会显著降低其性能。为应对这一挑战，我们提出一种新颖方法，该方法采用对抗鲁棒性评估技术来识别图中最易受噪声影响的节点。通过选取并构建由这些特别易受噪声干扰的节点组成的训练集，我们随后使用该训练集来训练图卷积网络（GCN）。实验结果表明，该策略能显著提升GCN的性能。