Graph Neural Networks (GNNs) are currently one of the most powerful types of neural network architectures. Their advantage lies in the ability to leverage both the graph topology, which represents the relationships between samples, and the features of the samples themselves. However, the given graph topology often contains noisy edges, and GNNs are vulnerable to noise in the graph structure. This issue remains unresolved. In this paper, we propose using adversarial robustness evaluation to select a small subset of robust nodes that are less affected by noise. We then only feed the features of these robust nodes, along with the KNN graph constructed from these nodes, into the GNN for classification. Additionally, we compute the centroids for each class. For the remaining non-robust nodes, we assign them to the class whose centroid is closest to them. Experimental results show that this method significantly improves the accuracy of GNNs.

翻译：图神经网络（GNNs）是目前最强大的神经网络架构类型之一。其优势在于能够同时利用表示样本间关系的图拓扑结构以及样本自身的特征。然而，给定的图拓扑通常包含噪声边，且GNNs对图结构中的噪声较为敏感。这一问题尚未得到解决。本文提出利用对抗鲁棒性评估来选取一个受噪声影响较小的鲁棒节点子集。随后，我们仅将这些鲁棒节点的特征以及基于这些节点构建的KNN图输入GNN进行分类。此外，我们计算每个类别的质心。对于其余的非鲁棒节点，我们将其分配给质心距离最近的类别。实验结果表明，该方法显著提升了GNNs的分类准确率。