Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are often affected by redundant and erroneous edges present within the constructed graphs. In this paper, we view these noisy edges as adversarial attack and propose to use a spectral adversarial robustness evaluation method to mitigate the impact of noisy edges on the performance of graph-based algorithms. Our method identifies the points that are less vulnerable to noisy edges and leverages only these robust points to perform graph-based algorithms. Our experiments demonstrate that our methodology is highly effective and outperforms state-of-the-art denoising methods by a large margin.

翻译：鉴于现有图构建方法无法为给定数据集生成完美图，图算法常受构建图中冗余与错误边的影响。本文将此类噪声边视为对抗攻击，并提出采用谱对抗鲁棒性评估方法来缓解噪声边对图算法性能的影响。该方法识别对噪声边敏感度较低的节点，并仅利用这些鲁棒节点执行图算法。实验表明，本方法具有显著效果，其性能大幅超越当前最先进的去噪方法。