通过对抗鲁棒性评估缓解噪声边对图基算法的影响
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation
AI总结：
针对图构建中噪声边影响图基算法的问题，提出谱对抗鲁棒性评估方法，识别鲁棒点并仅用其执行算法，实验证明该方法大幅优于现有去噪方法。
摘要：
鉴于现有图构建方法无法为给定数据集生成完美图，图基算法常受构建图中冗余和错误边的影响。本文将这些噪声边视为对抗攻击，提出使用谱对抗鲁棒性评估方法缓解噪声边对图基算法性能的影响。该方法识别出对噪声边较不脆弱的点，仅利用这些鲁棒点执行图基算法。实验表明，该方法高效，大幅优于最先进的去噪方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
